Repository: gsassatelli/explainable-rl (language: python)

Issue:
Agent skeleton



Discussion / hints:

Fix patch:
diff --git a/cumulative_reward.png b/cumulative_reward.png
new file mode 100644
index 0000000..9648925
Binary files /dev/null and b/cumulative_reward.png differ
diff --git a/evaluation.ipynb b/evaluation.ipynb
new file mode 100644
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--- /dev/null
+++ b/evaluation.ipynb
@@ -0,0 +1,2485 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.foundation.engine import Engine\n",
+    "from src.data_handler.data_handler import DataHandler\n",
+    "from src.explainability.pdp import PDP\n",
+    "from datetime import datetime\n",
+    "import ipdb"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define hyper-parameters for DS data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hyperparam_dict_ds_data = {\n",
+    "        'states': ['lead_time', 'length_of_stay',\n",
+    "                   'competitor_price_difference_bin', 'demand_bin'],\n",
+    "        'actions': ['price'],\n",
+    "        'bins': [10, 10, 4, 4, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.\n",
+    "        'rewards': ['reward'],\n",
+    "        'feature_types': {\n",
+    "            'lead_time': \"continuous\",\n",
+    "            'length_of_stay': \"continuous\",\n",
+    "            'competitor_price_difference_bin': \"discrete\",\n",
+    "            'demand_bin': \"discrete\",\n",
+    "            'price': \"continuous\",\n",
+    "            'reward': \"continuous\"\n",
+    "        },\n",
+    "        'n_samples': 100000,\n",
+    "        'data_path': 'data/ds-data/my_example_data.parquet',\n",
+    "        'col_delimiter': '|',\n",
+    "        'cols_to_normalise': ['lead_time', 'length_of_stay',\n",
+    "                   'competitor_price_difference_bin', 'demand_bin', 'price', 'reward'],\n",
+    "        'agent_type': 'q_learner',\n",
+    "        'env_type': 'strategic_pricing',\n",
+    "        'num_episodes': 50000,\n",
+    "        'num_steps': 1,\n",
+    "        'train_test_split': 0.2\n",
+    "    }\n",
+    "\n",
+    "hyperparam_dict = hyperparam_dict_ds_data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train agent (I interrupted at 50,000 episodes because it was taking too long):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14/04/2023 08:26:19: Load data\n",
+      "14/04/2023 08:26:19: Preprocess data\n",
+      "14/04/2023 08:26:21: Initialize Engine\n",
+      "14/04/2023 08:26:21: Create the world\n",
+      "Initialize environment\n",
+      "Create average rewards matrix\n",
+      "Initialize agent\n",
+      "14/04/2023 08:26:37: Train the agent on 100000 samples\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:14<00:00, 68.34it/s]\n",
+      "100%|██████████| 1000/1000 [00:06<00:00, 161.95it/s]\n",
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+      "100%|██████████| 1000/1000 [00:05<00:00, 166.84it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Load data\")\n",
+    "states = hyperparam_dict['states']\n",
+    "actions = hyperparam_dict['actions']\n",
+    "rewards = hyperparam_dict['rewards']\n",
+    "n_samples = hyperparam_dict['n_samples']\n",
+    "dh = DataHandler(data_path=hyperparam_dict['data_path'],\n",
+    "                    state_labels=states,\n",
+    "                    action_labels=actions,\n",
+    "                    reward_labels=rewards,\n",
+    "                    n_samples=n_samples)\n",
+    "\n",
+    "# Preprocess the data\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Preprocess data\")\n",
+    "dh.prepare_data_for_engine(col_delimiter=hyperparam_dict['col_delimiter'],\n",
+    "                            cols_to_normalise=hyperparam_dict[\n",
+    "                                'cols_to_normalise'])\n",
+    "\n",
+    "# Create engine\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "\n",
+    "print(f\"{timestamp}: Initialize Engine\")\n",
+    "engine = Engine(dh,\n",
+    "                agent_type=hyperparam_dict['agent_type'],\n",
+    "                env_type=hyperparam_dict['env_type'],\n",
+    "                num_episodes=hyperparam_dict['num_episodes'],\n",
+    "                num_steps=hyperparam_dict['num_steps'],\n",
+    "                bins=hyperparam_dict['bins'],\n",
+    "                train_test_split = hyperparam_dict['train_test_split']\n",
+    "                )\n",
+    "# Create world\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Create the world\")\n",
+    "engine.create_world()\n",
+    "\n",
+    "# Train agent\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Train the agent on {n_samples} samples\")\n",
+    "engine.train_agent(evaluate=True, n_eval_steps=1000)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate agent (get evaluation states, actions, rewards, and recommended actions and resulting rewards):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Return based on historical data: 147642.84877104228\n",
+      "Return based on agent policy: 135756.98058590954\n"
+     ]
+    }
+   ],
+   "source": [
+    "states, actions_hist, b_actions, rewards_hist, actions_agent, b_actions_agent, rewards_agent = \\\n",
+    "    engine.evaluate_agent()\n",
+    "\n",
+    "# Sum obtained reward optimal vs historical policy\n",
+    "import numpy as np\n",
+    "print(f\"Return based on historical data: {np.sum(rewards_hist)}\")\n",
+    "print(f\"Return based on agent policy: {np.sum(rewards_agent)}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get plot of cumulative reward vs iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots()\n",
+    "axes.plot([i*1000 for i in range(len(engine.agent_cumrewards))], engine.agent_cumrewards, label='q-learner')\n",
+    "axes.axhline(np.sum(rewards_hist), color='r',linestyle='--', label='historical baseline')\n",
+    "axes.grid()\n",
+    "axes.set_xlabel(\"episode\")\n",
+    "axes.set_ylabel(\"total reward\")\n",
+    "axes.legend()\n",
+    "axes.set_title(\"Cumulative Reward (Evaluation Set)\")\n",
+    "plt.savefig(\"cumulative_reward.png\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get plot of reward percentiles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rewards_agent_array = np.array([r[0] for r in rewards_agent])\n",
+    "agent_percentiles = np.percentile(rewards_agent_array, q=np.linspace(0,100,101))\n",
+    "\n",
+    "rewards_hist_array = np.array([r[0] for r in rewards_hist])\n",
+    "hist_percentiles = np.percentile(rewards_hist_array, q=np.linspace(0,100,101))\n",
+    "\n",
+    "fig, axes = plt.subplots()\n",
+    "axes.plot(np.linspace(0,100,101), agent_percentiles, label='q-learner')\n",
+    "axes.plot(np.linspace(0,100,101), hist_percentiles, color='r',linestyle='--', label='historical baseline')\n",
+    "\n",
+    "axes.grid()\n",
+    "axes.set_xlabel(\"probability\")\n",
+    "axes.set_ylabel(\"average reward\")\n",
+    "axes.legend()\n",
+    "axes.set_title(\"Reward Percentiles (Evaluation Set)\")\n",
+    "plt.savefig(\"reward_percentiles.png\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare actions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fcba9fbaa70>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "count_actions = np.zeros(shape=(10,10))\n",
+    "for a, h in zip(b_actions_agent, b_actions):\n",
+    "    count_actions[a,h] += 1\n",
+    "plt.imshow(count_actions, cmap='hot', interpolation='nearest', origin='lower')\n",
+    "plt.xlabel('agent action')\n",
+    "plt.ylabel('historical action')\n",
+    "plt.title('Historical vs Recommended actions')\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PDP plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14/04/2023 10:03:04: Show PDPs plots\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/home/usuari/Desktop/Imperial/mscai/Term 2/software-engineering-group/explainable-RL/evaluation.ipynb Cell 15\u001b[0m in \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mtimestamp\u001b[39m}\u001b[39;00m\u001b[39m: Show PDPs plots\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pdp \u001b[39m=\u001b[39m PDP(bins\u001b[39m=\u001b[39mengine\u001b[39m.\u001b[39menv\u001b[39m.\u001b[39mbins,\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m             minmax_scalars\u001b[39m=\u001b[39mdh\u001b[39m.\u001b[39mminmax_scalars,\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m             action_labels\u001b[39m=\u001b[39mactions,\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m             state_labels\u001b[39m=\u001b[39mstates)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m pdp\u001b[39m.\u001b[39;49mbuild_data_for_plots(engine\u001b[39m.\u001b[39;49magent\u001b[39m.\u001b[39;49mQ, engine\u001b[39m.\u001b[39;49magent\u001b[39m.\u001b[39;49mQ_num_samples)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m type_features \u001b[39m=\u001b[39m hyperparam_dict[\u001b[39m'\u001b[39m\u001b[39mfeature_types\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/usuari/Desktop/Imperial/mscai/Term%202/software-engineering-group/explainable-RL/evaluation.ipynb#X24sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m fig_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mPDP plots - All states\u001b[39m\u001b[39m\"\u001b[39m\n",
+      "File \u001b[0;32m~/Desktop/Imperial/mscai/Term 2/software-engineering-group/explainable-RL/src/explainability/pdp.py:58\u001b[0m, in \u001b[0;36mPDP.build_data_for_plots\u001b[0;34m(self, Q, Q_num_samples)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_digitized_pdp(Q, Q_num_samples)\n\u001b[1;32m     57\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_denorm_actions()\n\u001b[0;32m---> 58\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_denorm_states()\n",
+      "File \u001b[0;32m~/Desktop/Imperial/mscai/Term 2/software-engineering-group/explainable-RL/src/explainability/pdp.py:122\u001b[0m, in \u001b[0;36mPDP._get_denorm_states\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[39m# Divide by number of bins to get a value between [0,1]\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[39m# which can then be inputted into the scaler\u001b[39;00m\n\u001b[1;32m    121\u001b[0m dig_values \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(\u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(n_bins))) \u001b[39m/\u001b[39m n_bins\n\u001b[0;32m--> 122\u001b[0m scaler \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_minmax_scalars[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_state_labels[i]]\n\u001b[1;32m    123\u001b[0m denorm_state \u001b[39m=\u001b[39m scaler\u001b[39m.\u001b[39minverse_transform(dig_values\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m))\n\u001b[1;32m    124\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_denorm_states\u001b[39m.\u001b[39mappend(denorm_state)\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot PDPs\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Show PDPs plots\")\n",
+    "pdp = PDP(bins=engine.env.bins,\n",
+    "            minmax_scalars=dh.minmax_scalars,\n",
+    "            action_labels=actions,\n",
+    "            state_labels=states)\n",
+    "pdp.build_data_for_plots(engine.agent.Q, engine.agent.Q_num_samples)\n",
+    "type_features = hyperparam_dict['feature_types']\n",
+    "fig_name = \"PDP plots - All states\"\n",
+    "pdp.plot_pdp(states_names=states, fig_name=fig_name,\n",
+    "                type_features=type_features, savefig=True, all_states=True)\n",
+    "fig_name = \"PDP plots - Visited states\"\n",
+    "pdp.plot_pdp(states_names=states, fig_name=fig_name,\n",
+    "                type_features=type_features, savefig=True, all_states=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b_actions = engine.env.bin_states(actions, idxs=list(range(engine.env.state_dim, engine.env.state_dim+engine.env.action_dim)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+     "metadata": {},
+     "output_type": "execute_result"
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+  {
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+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
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+       " ([3], [3]),\n",
+       " ([4], [4]),\n",
+       " ([3], [4]),\n",
+       " ([2], [2]),\n",
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+       " ([1], [0]),\n",
+       " ([3], [3]),\n",
+       " ([5], [0]),\n",
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+       " ([3], [4]),\n",
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+       " ([2], [0]),\n",
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+       " ([1], [0]),\n",
+       " ([3], [3]),\n",
+       " ([2], [0]),\n",
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+       " ([2], [1]),\n",
+       " ([3], [0]),\n",
+       " ([7], [6]),\n",
+       " ([5], [3]),\n",
+       " ([3], [3]),\n",
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+       " ([2], [1]),\n",
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+       " ([2], [2]),\n",
+       " ([6], [5]),\n",
+       " ([3], [3]),\n",
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+       " ([4], [5]),\n",
+       " ([2], [0]),\n",
+       " ([4], [4]),\n",
+       " ([1], [3]),\n",
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+       " ([2], [4]),\n",
+       " ([3], [2]),\n",
+       " ([1], [1]),\n",
+       " ([3], [3]),\n",
+       " ([3], [3]),\n",
+       " ([1], [0]),\n",
+       " ([2], [2]),\n",
+       " ([1], [3]),\n",
+       " ([3], [3]),\n",
+       " ([2], [4]),\n",
+       " ([4], [5]),\n",
+       " ([3], [4]),\n",
+       " ([3], [4]),\n",
+       " ([6], [0]),\n",
+       " ([4], [4]),\n",
+       " ([3], [5]),\n",
+       " ([1], [0]),\n",
+       " ([2], [0]),\n",
+       " ([3], [2]),\n",
+       " ([2], [0]),\n",
+       " ([3], [4]),\n",
+       " ([1], [1]),\n",
+       " ([3], [3]),\n",
+       " ([3], [2]),\n",
+       " ([2], [0]),\n",
+       " ([4], [2]),\n",
+       " ([0], [0]),\n",
+       " ([5], [4]),\n",
+       " ([2], [1]),\n",
+       " ([2], [1]),\n",
+       " ([3], [3]),\n",
+       " ([2], [1]),\n",
+       " ([1], [0]),\n",
+       " ([2], [3]),\n",
+       " ([3], [4]),\n",
+       " ([4], [2]),\n",
+       " ([4], [3]),\n",
+       " ([3], [2]),\n",
+       " ([2], [4]),\n",
+       " ([5], [4]),\n",
+       " ([1], [2]),\n",
+       " ([3], [3]),\n",
+       " ([5], [0]),\n",
+       " ([1], [0]),\n",
+       " ([5], [0]),\n",
+       " ([2], [0]),\n",
+       " ([3], [2]),\n",
+       " ([2], [0]),\n",
+       " ([3], [3]),\n",
+       " ([6], [6]),\n",
+       " ([3], [1]),\n",
+       " ([2], [2]),\n",
+       " ([3], [3]),\n",
+       " ([4], [0]),\n",
+       " ([6], [5]),\n",
+       " ([3], [0]),\n",
+       " ([2], [4]),\n",
+       " ([3], [3]),\n",
+       " ([5], [0]),\n",
+       " ([2], [0]),\n",
+       " ([2], [2]),\n",
+       " ([2], [4]),\n",
+       " ([1], [0]),\n",
+       " ([3], [4]),\n",
+       " ([4], [1]),\n",
+       " ([2], [3]),\n",
+       " ([3], [0]),\n",
+       " ([1], [0]),\n",
+       " ([3], [0]),\n",
+       " ([3], [4]),\n",
+       " ([3], [3]),\n",
+       " ([3], [0]),\n",
+       " ([2], [4]),\n",
+       " ([4], [2]),\n",
+       " ([4], [3]),\n",
+       " ([1], [0]),\n",
+       " ([4], [2]),\n",
+       " ([2], [1]),\n",
+       " ([1], [0]),\n",
+       " ([2], [0]),\n",
+       " ([3], [0]),\n",
+       " ([2], [0]),\n",
+       " ([3], [3]),\n",
+       " ([3], [4]),\n",
+       " ([2], [3]),\n",
+       " ([2], [2]),\n",
+       " ([2], [0]),\n",
+       " ([4], [3]),\n",
+       " ([4], [4]),\n",
+       " ([2], [3]),\n",
+       " ([4], [5]),\n",
+       " ([5], [6]),\n",
+       " ([2], [0]),\n",
+       " ([2], [2]),\n",
+       " ([3], [4]),\n",
+       " ([5], [0]),\n",
+       " ([4], [0]),\n",
+       " ([3], [0]),\n",
+       " ([1], [0]),\n",
+       " ([2], [0]),\n",
+       " ([4], [5]),\n",
+       " ([4], [5]),\n",
+       " ([5], [5]),\n",
+       " ([3], [1]),\n",
+       " ([3], [1]),\n",
+       " ([3], [2]),\n",
+       " ([2], [0]),\n",
+       " ([3], [5]),\n",
+       " ([3], [3]),\n",
+       " ([3], [4]),\n",
+       " ([4], [0]),\n",
+       " ([3], [1]),\n",
+       " ([5], [5]),\n",
+       " ([1], [2]),\n",
+       " ([2], [3]),\n",
+       " ([4], [5]),\n",
+       " ([4], [3]),\n",
+       " ([2], [2]),\n",
+       " ([4], [0]),\n",
+       " ([3], [2]),\n",
+       " ([1], [0]),\n",
+       " ([3], [3]),\n",
+       " ([3], [4]),\n",
+       " ([5], [6]),\n",
+       " ([3], [3]),\n",
+       " ([5], [0]),\n",
+       " ([3], [4]),\n",
+       " ([3], [2]),\n",
+       " ([3], [4]),\n",
+       " ([4], [4]),\n",
+       " ([3], [2]),\n",
+       " ([2], [0]),\n",
+       " ([4], [7]),\n",
+       " ([4], [4]),\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(zip(b_actions, b_actions_agent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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([100.0], [48.65]), ([68.0], [48.65]), ([70.0], [79.25]), ([38.0], [48.65]), ([72.8], [18.05]), ([72.0], [48.65]), ([84.60000000000001], [125.14999999999999]), ([36.4], [18.05]), ([73.8], [33.35]), ([73.8], [18.05]), ([64.79999999999998], [94.55]), ([68.4], [79.25]), ([52.6], [48.65]), ([39.6], [48.65]), ([33.4], [18.05]), ([69.2], [79.25]), ([76.2], [18.05]), ([53.599999999999994], [63.94999999999999]), ([65.4], [63.94999999999999]), ([132.0], [125.14999999999999]), ([62.99999999999999], [63.94999999999999]), ([54.199999999999996], [48.65]), ([53.4], [48.65]), ([75.2], [94.55]), ([34.6], [79.25]), ([53.99999999999999], [48.65]), ([40.2], [18.05]), ([92.8], [94.55]), ([43.20000000000001], [48.65]), ([32.6], [18.05]), ([53.199999999999996], [48.65]), ([71.2], [18.05]), ([57.4], [48.65]), ([78.79999999999998], [79.25]), ([58.599999999999994], [94.55]), ([27.6], [18.05]), ([44.800000000000004], [18.05]), ([95.39999999999999], [125.14999999999999]), ([42.4], [48.65]), ([69.4], [63.94999999999999]), ([68.19999999999999], [18.05]), ([44.400000000000006], [48.65]), ([76.0], [63.94999999999999]), ([54.99999999999999], [48.65]), ([56.8], [63.94999999999999]), ([75.59999999999998], [48.65]), ([34.4], [18.05]), ([99.6], [125.14999999999999]), ([27.8], [18.05]), ([48.8], [18.05]), ([109.4], [94.55]), ([81.8], [94.55]), ([62.599999999999994], [79.25]), ([65.2], [48.65]), ([31.6], [48.65]), ([36.8], [18.05]), ([34.4], [18.05]), ([58.99999999999999], [63.94999999999999]), ([30.0], [18.05]), ([46.2], [63.94999999999999]), ([37.8], [79.25]), ([64.79999999999998], [79.25]), ([64.4], [33.35]), ([65.59999999999998], [94.55]), ([51.2], [63.94999999999999]), ([77.19999999999999], [63.94999999999999]), ([81.6], [79.25]), ([64.19999999999999], [94.55]), ([52.4], [18.05]), ([48.8], [18.05]), ([66.8], [18.05]), ([65.79999999999998], [18.05]), ([87.0], [79.25]), ([62.199999999999996], [48.65]), ([101.8], [79.25]), ([30.2], [18.05]), ([52.6], [63.94999999999999]), ([32.2], [48.65]), ([61.2], [79.25]), ([76.39999999999999], [79.25]), ([77.99999999999999], [63.94999999999999]), ([35.8], [18.05]), ([71.0], [18.05]), ([67.8], [18.05]), ([130.2], [125.14999999999999]), ([89.8], [79.25]), ([66.2], [79.25]), ([55.6], [63.94999999999999]), ([66.4], [18.05]), ([59.39999999999999], [63.94999999999999]), ([54.99999999999999], [48.65]), ([56.8], [94.55]), ([55.2], [79.25]), ([49.99999999999999], [79.25]), ([32.0], [18.05]), ([84.60000000000001], [94.55]), ([61.0], [94.55]), ([55.2], [79.25]), ([41.6], [48.65]), ([46.2], [79.25]), ([54.199999999999996], [48.65]), ([53.99999999999999], [48.65]), ([51.2], [79.25]), ([82.4], [18.05]), ([79.59999999999998], [79.25]), ([26.2], [48.65]), ([53.599999999999994], [79.25]), ([46.0], [18.05]), ([39.79999999999999], [48.65]), ([27.8], [18.05]), ([50.79999999999999], [48.65]), ([58.39999999999999], [63.94999999999999]), ([28.999999999999996], [18.05]), ([47.2], [18.05]), ([62.8], [18.05]), ([86.6], [18.05]), ([65.59999999999998], [63.94999999999999]), ([79.59999999999998], [94.55]), ([63.199999999999996], [48.65]), ([58.79999999999999], [48.65]), ([75.0], [33.35]), ([33.4], [18.05]), ([53.8], [63.94999999999999]), ([69.79999999999998], [18.05]), ([57.99999999999999], [63.94999999999999]), ([59.79999999999999], [79.25]), ([51.800000000000004], [18.05]), ([45.4], [18.05]), ([58.199999999999996], [48.65]), ([77.6], [109.85]), ([56.2], [48.65]), ([70.2], [18.05]), ([84.4], [48.65]), ([64.19999999999999], [63.94999999999999]), ([63.99999999999999], [63.94999999999999]), ([66.4], [48.65]), ([95.6], [48.65]), ([86.6], [63.94999999999999]), ([30.0], [48.65]), ([59.999999999999986], [48.65]), ([48.0], [63.94999999999999]), ([91.6], [18.05]), ([69.2], [79.25]), ([55.2], [63.94999999999999]), ([41.0], [79.25]), ([40.8], [18.05]), ([48.4], [48.65]), ([54.599999999999994], [48.65]), ([50.60000000000001], [48.65]), ([84.4], [18.05]), ([55.2], [79.25]), ([34.8], [18.05]), ([38.599999999999994], [18.05]), ([63.599999999999994], [48.65]), ([59.999999999999986], [63.94999999999999]), ([76.8], [18.05]), ([46.4], [48.65]), ([81.8], [109.85]), ([53.199999999999996], [48.65]), ([103.4], [79.25]), ([62.8], [94.55]), ([52.0], [48.65]), ([52.4], [79.25]), ([42.0], [18.05]), ([76.6], [79.25]), ([52.2], [48.65]), ([73.4], [63.94999999999999]), ([52.8], [63.94999999999999]), ([72.0], [48.65]), ([62.8], [94.55]), ([60.2], [48.65]), ([58.99999999999999], [63.94999999999999]), ([85.2], [79.25]), ([43.0], [63.94999999999999]), ([51.6], [63.94999999999999]), ([37.2], [18.05]), ([91.8], [109.85]), ([33.4], [79.25]), ([75.4], [94.55]), ([61.0], [48.65]), ([78.2], [109.85]), ([62.0], [79.25]), ([61.8], [48.65]), ([64.19999999999999], [18.05]), ([58.39999999999999], [48.65]), ([62.4], [48.65]), ([51.00000000000001], [79.25]), ([57.8], [48.65]), ([49.199999999999996], [94.55]), ([62.4], [79.25]), ([54.99999999999999], [94.55]), ([64.19999999999999], [63.94999999999999]), ([51.800000000000004], [63.94999999999999]), ([58.599999999999994], [79.25]), ([32.2], [18.05]), ([38.599999999999994], [48.65]), ([63.199999999999996], [63.94999999999999]), ([64.6], [79.25]), ([78.6], [79.25]), ([65.79999999999998], [18.05]), ([58.599999999999994], [63.94999999999999]), ([45.6], [79.25]), ([70.4], [94.55]), ([72.6], [79.25]), ([59.59999999999999], [79.25]), ([93.0], [125.14999999999999]), ([45.6], [18.05]), ([96.4], [79.25]), ([71.8], [18.05]), ([57.99999999999999], [63.94999999999999]), ([57.99999999999999], [79.25]), ([52.0], [79.25]), ([71.2], [79.25]), ([75.2], [48.65]), ([45.8], [18.05]), ([87.0], [48.65]), ([53.599999999999994], [79.25]), ([61.0], [79.25]), ([42.8], [18.05]), ([55.6], [63.94999999999999]), ([68.4], [109.85]), ([64.99999999999999], [79.25]), ([46.6], [79.25]), ([43.8], [18.05]), ([46.4], [63.94999999999999]), ([41.0], [18.05]), ([81.6], [18.05]), ([45.6], [18.05]), ([93.6], [18.05]), ([55.39999999999999], [79.25]), ([98.2], [94.55]), ([59.999999999999986], [48.65]), ([59.39999999999999], [79.25]), ([61.6], [109.85]), ([47.8], [18.05]), ([45.2], [33.35]), ([28.4], [18.05]), ([59.59999999999999], [63.94999999999999]), ([82.6], [79.25]), ([88.80000000000001], [94.55]), ([70.0], [94.55]), ([75.4], [63.94999999999999]), ([76.39999999999999], [48.65]), ([19.0], [18.05]), ([73.19999999999999], [63.94999999999999]), ([55.79999999999999], [48.65]), ([47.4], [79.25]), ([40.6], [18.05]), ([52.2], [48.65]), ([74.2], [94.55]), ([62.199999999999996], [63.94999999999999]), ([108.2], [125.14999999999999]), ([34.6], [18.05]), ([49.199999999999996], [18.05]), ([95.39999999999999], [125.14999999999999]), ([62.99999999999999], [48.65]), ([106.0], [63.94999999999999]), ([84.2], [63.94999999999999]), ([69.79999999999998], [79.25]), ([79.59999999999998], [94.55]), ([44.2], [18.05]), ([61.8], [18.05]), ([90.8], [79.25]), ([73.4], [109.85]), ([56.2], [48.65]), ([43.8], [18.05]), ([49.599999999999994], [48.65]), ([84.60000000000001], [79.25]), ([70.8], [79.25]), ([52.0], [48.65]), ([101.6], [94.55]), ([57.4], [48.65]), ([50.79999999999999], [48.65]), ([59.39999999999999], [18.05]), ([78.79999999999998], [63.94999999999999]), ([72.0], [63.94999999999999]), ([80.4], [18.05]), ([44.2], [18.05]), ([90.2], [109.85]), ([62.199999999999996], [48.65]), ([45.8], [79.25]), ([50.79999999999999], [63.94999999999999]), ([54.199999999999996], [18.05]), ([54.199999999999996], [94.55]), ([65.4], [48.65]), ([35.8], [18.05]), ([37.6], [18.05]), ([31.8], [18.05]), ([53.599999999999994], [63.94999999999999]), ([60.399999999999984], [48.65]), ([75.59999999999998], [18.05]), ([88.80000000000001], [94.55]), ([51.2], [18.05]), ([64.19999999999999], [79.25]), ([47.4], [18.05]), ([34.4], [18.05]), ([83.2], [18.05]), ([54.99999999999999], [63.94999999999999]), ([52.4], [18.05]), ([40.2], [18.05]), ([59.999999999999986], [18.05]), ([62.599999999999994], [79.25]), ([62.4], [79.25]), ([51.800000000000004], [79.25]), ([41.0], [18.05]), ([41.8], [63.94999999999999]), ([46.4], [63.94999999999999]), ([55.39999999999999], [48.65]), ([63.599999999999994], [63.94999999999999]), ([67.39999999999999], [63.94999999999999]), ([44.2], [48.65]), ([65.2], [48.65]), ([89.8], [94.55]), ([44.00000000000001], [63.94999999999999]), ([63.599999999999994], [18.05]), ([83.2], [18.05]), ([95.6], [94.55]), ([43.4], [63.94999999999999]), ([92.0], [79.25]), ([70.2], [63.94999999999999]), ([61.4], [18.05]), ([63.199999999999996], [94.55]), ([33.0], [18.05]), ([58.199999999999996], [48.65]), ([44.400000000000006], [18.05]), ([62.599999999999994], [63.94999999999999]), ([68.0], [63.94999999999999]), ([65.59999999999998], [48.65]), ([30.0], [63.94999999999999]), ([59.999999999999986], [48.65]), ([56.6], [79.25]), ([43.8], [79.25]), ([60.79999999999998], [48.65]), ([65.59999999999998], [79.25]), ([45.4], [63.94999999999999]), ([81.0], [63.94999999999999]), ([44.800000000000004], [63.94999999999999]), ([43.20000000000001], [33.35]), ([92.6], [109.85]), ([63.79999999999999], [63.94999999999999]), ([53.4], [48.65]), ([54.599999999999994], [79.25]), ([76.8], [18.05]), ([66.59999999999998], [63.94999999999999]), ([64.19999999999999], [79.25]), ([58.99999999999999], [63.94999999999999]), ([69.4], [79.25]), ([66.0], [48.65]), ([76.6], [94.55]), ([29.6], [79.25]), ([77.4], [79.25]), ([28.599999999999998], [18.05]), ([53.599999999999994], [63.94999999999999]), ([88.6], [18.05]), ([59.999999999999986], [48.65]), ([72.6], [79.25]), ([59.39999999999999], [79.25]), ([59.999999999999986], [18.05]), ([43.60000000000001], [63.94999999999999]), ([44.6], [48.65]), ([90.0], [63.94999999999999]), ([54.79999999999999], [79.25]), ([78.6], [63.94999999999999]), ([47.0], [18.05]), ([54.599999999999994], [63.94999999999999]), ([54.599999999999994], [94.55]), ([39.6], [48.65]), ([57.4], [63.94999999999999]), ([65.2], [79.25]), ([85.8], [94.55]), ([69.4], [63.94999999999999]), ([48.4], [48.65]), ([61.4], [48.65])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "list(zip(actions, actions_agent))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/main.py b/main.py
index 8d5320e..8f13869 100644
--- a/main.py
+++ b/main.py
@@ -3,71 +3,100 @@
 from src.explainability.pdp import PDP
 from src.explainability.shap_values import ShapValues
 from datetime import datetime
+import ipdb
 
+def policy_deviation():
+    pass
 
-def run_all(hyperparam_dict):
+def run_all(hyperparam_dict, verbose=True, show_plots=True):
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
     print(f"{timestamp}: Load data")
-    states = hyperparam_dict['states']
-    actions = hyperparam_dict['actions']
-    rewards = hyperparam_dict['rewards']
+    state_labels = hyperparam_dict['states']
+    action_labels = hyperparam_dict['actions']
+    reward_labels = hyperparam_dict['rewards']
     n_samples = hyperparam_dict['n_samples']
     n_episodes = hyperparam_dict['num_episodes']
     dh = DataHandler(data_path=hyperparam_dict['data_path'],
-                     state_labels=states,
-                     action_labels=actions,
-                     reward_labels=rewards,
+                     state_labels=state_labels,
+                     action_labels=action_labels,
+                     reward_labels=reward_labels,
                      n_samples=n_samples)
 
     # Preprocess the data
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-    print(f"{timestamp}: Preprocess data")
+    if verbose:
+        print(f"{timestamp}: Preprocess data")
     dh.prepare_data_for_engine(col_delimiter=hyperparam_dict['col_delimiter'],
                                cols_to_normalise=hyperparam_dict[
                                    'cols_to_normalise'])
 
     # Create engine
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-
-    print(f"{timestamp}: Initialize Engine")
+    if verbose:
+        print(f"{timestamp}: Initialize Engine")
     engine = Engine(dh,
                     agent_type=hyperparam_dict['agent_type'],
                     env_type=hyperparam_dict['env_type'],
                     num_episodes=hyperparam_dict['num_episodes'],
                     num_steps=hyperparam_dict['num_steps'],
-                    bins=hyperparam_dict['bins']
+                    bins=hyperparam_dict['bins'],
+                    train_test_split = hyperparam_dict['train_test_split']
                     )
     # Create world
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-    print(f"{timestamp}: Create the world")
+    if verbose:
+        print(f"{timestamp}: Create the world")
     engine.create_world()
 
     # Train agent
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-    print(f"{timestamp}: Train the agent on {n_samples} samples and {n_episodes} episodes")
+    if verbose:
+        print(f"{timestamp}: Train the agent on {n_samples} samples")
     engine.train_agent()
 
+    ###########################################################
+    ################ Evaluate agent ###########################
+    ###########################################################
+
+    # TODO: denorm states, actions and rewards (using datahandler's inverse scaling)
+
+    states, actions, b_actions, rewards_hist, actions_agent, b_actions_agent, rewards_agent = \
+        engine.evaluate_agent()
+    # Sum obtained reward optimal vs historical policy
+    import numpy as np
+    print(f"Return based on historical data: {np.sum(rewards_hist)}")
+    print(f"Return based on agent policy: {np.sum(rewards_agent)}")
+    
+    import matplotlib.pyplot as plt
+    plt.scatter(actions, actions_agent)
+    plt.savefig('policy.png')
+
+
+    ###########################################################
+    ################# End of evaluation #######################
+    ###########################################################
+    
     # Plot PDPs
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-    print(f"{timestamp}: Show PDPs plots")
+    if verbose:
+        print(f"{timestamp}: Show PDPs plots")
     pdp = PDP(bins=engine.env.bins,
               minmax_scalars=dh.minmax_scalars,
-              action_labels=actions,
-              state_labels=states)
+              action_labels=action_labels,
+              state_labels=state_labels)
     pdp.build_data_for_plots(engine.agent.Q, engine.agent.Q_num_samples)
     type_features = hyperparam_dict['feature_types']
     fig_name = "PDP plots - All states"
-    pdp.plot_pdp(states_names=states, fig_name=fig_name,
+    pdp.plot_pdp(states_names=state_labels, fig_name=fig_name,
                  type_features=type_features, savefig=True, all_states=True)
-    if False:
-        fig_name = "PDP plots - Visited states"
-        pdp.plot_pdp(states_names=states, fig_name=fig_name,
+    fig_name = "PDP plots - Visited states"
+    pdp.plot_pdp(states_names=state_labels, fig_name=fig_name,
                  type_features=type_features, savefig=True, all_states=False)
 
     # Plot SHAP values
     timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
     print(f"{timestamp}: Show SHAP values plots")
-    shap_values = ShapValues(sample=[8, 3, 1, 1], features=states, env=engine.env,
+    shap_values = ShapValues(sample=[8, 3, 1, 1], features=state_labels, env=engine.env,
                              Q=engine.agent.Q, minmax_scalars=dh.minmax_scalars, action=actions)
     print(shap_values.compute_shap_values())
 
@@ -77,7 +106,8 @@ def run_all(hyperparam_dict):
         'states': ['lead_time', 'length_of_stay',
                    'competitor_price_difference_bin', 'demand_bin'],
         'actions': ['price'],
-        'bins': [10, 10, 4, 4, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.
+        'bins': [10, 10, 4, 4, 10],
+        # TODO: these correspond to the states and actions. Probably should change to a dict.
         'rewards': ['reward'],
         'feature_types': {
             'lead_time': "continuous",
@@ -87,15 +117,16 @@ def run_all(hyperparam_dict):
             'price': "continuous",
             'reward': "continuous"
         },
-        'n_samples': 500000,
+        'n_samples': 1000,
         'data_path': 'data/ds-data/my_example_data.parquet',
         'col_delimiter': '|',
         'cols_to_normalise': ['lead_time', 'length_of_stay',
-                   'competitor_price_difference_bin', 'demand_bin', 'price', 'reward'],
+                              'competitor_price_difference_bin', 'demand_bin', 'price', 'reward'],
         'agent_type': 'q_learner',
         'env_type': 'strategic_pricing',
-        'num_episodes': 20000,
-        'num_steps': 1
+        'num_episodes': 1000,
+        'num_steps': 1,
+        'train_test_split': 0.2
     }
 
     hyperparam_dict_kaggle_data = {
@@ -109,15 +140,16 @@ def run_all(hyperparam_dict):
             'price': "continuous",
             'revenue': "continuous"
         },
-        'bins': [10, 2, 2, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.
+        'bins': [10, 2, 2, 10],  # TODO: these correspond to the states and actions. Probably should change to a dict.
         'n_samples': 20000,
         'data_path': 'data/kaggle-dummy-dataset/train.csv',
         'col_delimiter': '|',
         'cols_to_normalise': ['competitorPrice', 'adFlag', 'availability', 'price'],
         'agent_type': 'q_learner',
         'env_type': 'strategic_pricing',
-        'num_episodes': 100,
-        'num_steps': 1
+        'num_episodes': 10,
+        'num_steps': 1,
+        'train_test_split': 0.2
     }
     for i in range(1):
         run_all(hyperparam_dict_ds_data)
diff --git a/reward_percentiles.png b/reward_percentiles.png
new file mode 100644
index 0000000..1ff7d92
Binary files /dev/null and b/reward_percentiles.png differ
diff --git a/src/data_handler/data_handler.py b/src/data_handler/data_handler.py
index a57ef30..ebc1f07 100644
--- a/src/data_handler/data_handler.py
+++ b/src/data_handler/data_handler.py
@@ -11,7 +11,8 @@ class DataHandler:
     """
 
     __slots__ = ["data_path", "dataset", "_normalised_cols", "minmax_scalars",
-                 "_state_labels", "_action_labels", "_reward_labels", "mdp_data", "_n_samples"]
+                 "state_labels", "action_labels", "reward_labels", "mdp_data",
+                 "mdp_data_test", "_n_samples"]
 
     def __init__(self, data_path,
                  state_labels,
@@ -31,10 +32,11 @@ def __init__(self, data_path,
         self.dataset = None
         self._normalised_cols = []
         self.minmax_scalars = {}
-        self._state_labels = state_labels
-        self._action_labels = action_labels
-        self._reward_labels = reward_labels
+        self.state_labels = state_labels
+        self.action_labels = action_labels
+        self.reward_labels = reward_labels
         self.mdp_data = None
+        self.mdp_data_test = None
 
     def prepare_data_for_engine(self, col_delimiter=',',
                                 cols_to_normalise=None):
@@ -64,7 +66,8 @@ def load_data(self, delimiter=','):
 
     def preprocess_data(self,
                         normalisation=True,
-                        columns_to_normalise=None):
+                        columns_to_normalise=None,
+                        train_test_split=0.2):
         """Preprocess data into state, action and reward spaces.
         Preprocessing applies shuffling, normalisation (if selected) and
         splits the dataset into states, actions and rewards.
@@ -79,12 +82,17 @@ def preprocess_data(self,
         if normalisation:
             self.normalise_dataset(cols_to_norm=columns_to_normalise)
 
-        s = self.dataset[self._state_labels]
-        a = self.dataset[self._action_labels]
-        r = self.dataset[self._reward_labels]
+        s = self.dataset[self.state_labels]
+        a = self.dataset[self.action_labels]
+        r = self.dataset[self.reward_labels]
 
         self.mdp_data = pd.concat({'s': s, 'a': a, 'r': r}, axis=1)
-        self.mdp_data = self.mdp_data[:self._n_samples]
+        self.mdp_data = self.mdp_data.sample(self._n_samples)
+
+        # split into train-test
+        split_indx = int(self._n_samples*train_test_split)
+        self.mdp_data_test = self.mdp_data[:split_indx]
+        self.mdp_data = self.mdp_data[split_indx:]
 
     def normalise_dataset(self, cols_to_norm=None):
         """Normalise the dataset to centre with mean zero and variance one.
@@ -104,26 +112,51 @@ def reverse_norm(self):
         for col in self._normalised_cols:
             self._inverse_transform_col(col_name=col)
 
-    def get_actions(self):
+    def get_actions(self,
+                    split='train'):
         """Get the actions taken in the dataset.
+
+        Args:
+            split (str): specifies train or test split
+
         Returns:
             pd.DataFrame of the actions.
         """
-        return self.mdp_data['a']
+        if split == 'train':
+            return self.mdp_data['a']
+        else:
+            return self.mdp_data_test['a']
 
-    def get_rewards(self):
+    def get_rewards(self,
+                    split='train'):
         """Get the rewards taken in the dataset.
+
+        Args:
+            split (str): specifies train or test split
+        
         Returns:
             pd.DataFrame of the rewards.
         """
-        return self.mdp_data['r']
+        if split == 'train':
+            return self.mdp_data['r']
+        else:
+            return self.mdp_data_test['r']
 
-    def get_states(self):
+    def get_states(self,
+                   split='train'):
         """Get the states taken in the dataset.
+
+        Args:
+            split (str): specifies train or test split
+        
         Returns:
             pd.DataFrame of the states.
         """
-        return self.mdp_data['s']
+        if split == 'train':
+            return self.mdp_data['s']
+        else:
+            return self.mdp_data_test['s']
+
 
     def _filter_data(self):
         """Filter the dataset.
@@ -146,6 +179,8 @@ def _inverse_transform_col(self, col_name: str):
 
     def _fit_standard_scalars(self):
         """Train the sklearn MinMaxScaler and store one per column.
+
+        TODO: fit only using train data and not test data
         """
         for col in self.dataset:
             scalar = MinMaxScaler()
diff --git a/src/documentation/src.performance.rst b/src/documentation/src.performance.rst
index 6b44779..dfd934c 100644
--- a/src/documentation/src.performance.rst
+++ b/src/documentation/src.performance.rst
@@ -4,10 +4,10 @@ src.performance package
 Submodules
 ----------
 
-src.performance.full\_flow module
+src.performance.performance\_evaluator module
 ---------------------------------
 
-.. automodule:: src.performance.full_flow
+.. automodule:: src.performance.performance_evaluator
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/src/foundation/agent.py b/src/foundation/agent.py
index 76ee601..2813575 100644
--- a/src/foundation/agent.py
+++ b/src/foundation/agent.py
@@ -4,6 +4,7 @@
 import random
 from datetime import datetime
 import ipdb
+from tqdm import tqdm
 
 from src.foundation.utils import *
 from src.foundation.super_classes import Agent
@@ -47,7 +48,7 @@ def fit(self, n_episodes, n_steps, lr=0.1, lr_decay=0.05, lr_min=0.01,
         if verbose:
             print("Apply q-learning and update q-table")
 
-        for _ in range(n_episodes):
+        for i in tqdm(range(n_episodes)):
 
             self.state = self.env.reset()
 
@@ -58,6 +59,7 @@ def fit(self, n_episodes, n_steps, lr=0.1, lr_decay=0.05, lr_min=0.01,
             lr = decay_param(lr, lr_decay, lr_min)
             epsilon = decay_param(epsilon, epsilon_decay, epsilon_min)
 
+
         if verbose:
             timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
             print(f"{timestamp}: Finished training :) ! \n"
@@ -82,6 +84,8 @@ def create_tables(self,
         # create q-table
         self._init_q_table()
         self.state_to_action = self.env.state_to_action
+    
+
 
     def _epsilon_greedy_policy(self,
                                state=None,
@@ -90,7 +94,6 @@ def _epsilon_greedy_policy(self,
 
         Args:
             state (list): current state of the agent.
-            state_str (string): the state as a string.
             epsilon (float): the exploration parameter.
 
         Returns:
@@ -160,3 +163,46 @@ def _update_q_values(self,
             q_current + lr * (reward + self.gamma * q_next - q_current)
 
         self.Q_num_samples[index_current] += 1
+    
+    def predict_actions(self,
+                states, 
+                epsilon=0):
+        """ Predict action for a list of states using epislon-greedy policy.
+        
+        Args:
+            states (list): States (binned).
+            epislon (float): Epislon of epislon-greedy policy.
+                Defaults to 0 for pure exploitation.
+        
+        Returns:
+            actions (list): List of recommended actions
+        """
+        actions = []
+        for state in states:
+            action = self._epsilon_greedy_policy(state, epsilon)
+            actions.append([action])
+
+        return actions
+    
+    def predict_rewards(self,
+                    states,
+                    actions):
+        """ Predict reward for a list of state-actions.
+         
+        This function uses the avg reward matrix (which simulates a real-life scenario)
+        
+        Args:
+            states (list): States (binned).
+            actions (list): Actions (binned).
+        
+        Returns:
+            rewards (list): List of recommended actions
+        """
+
+        rewards = []
+        for state, action in zip(states, actions):
+            _, _, reward, _ = self.env.step(state, action)
+            rewards.append([reward[0]])
+        
+        return rewards
+
diff --git a/src/foundation/engine.py b/src/foundation/engine.py
index 6580e50..fd8aa93 100644
--- a/src/foundation/engine.py
+++ b/src/foundation/engine.py
@@ -1,11 +1,17 @@
 # Import environment and agent
 from src.foundation.agent import QLearningAgent
 from src.foundation.environment import StrategicPricingMDP
+import numpy as np
+import ipdb 
 
 class Engine:
 
     __slots__ = ["dh", "agent_type", "env_type", "agent", "env", "gamma",
-                 "episode_flag", "num_episodes", "num_steps", "policy", "q_table", "bins"]
+                 "episode_flag", "num_episodes", "num_steps", "policy", 
+                 "q_table", "bins", "train_test_split", "agent_cumrewards",
+                 "hist_cumrewards", "_eval_states", "_eval_actions","_eval_rewards",
+                 "_eval_b_states","_eval_state_dims","_eval_action_dims"]
+    
 
     def __init__(self, 
                  dh,
@@ -14,6 +20,7 @@ def __init__(self,
                  num_episodes,
                  num_steps,
                  bins,
+                 train_test_split,
                  gamma=0.9):
         """Initilize engine class.
 
@@ -26,6 +33,7 @@ def __init__(self,
             bins (int): List of bins per state/action to discretize the state
                         space.
             gamma (float): Discount factor
+            train_test_split (float): proportion of test data
         """
         # Save data handler
         self.dh = dh
@@ -77,14 +85,27 @@ def create_env(self):
         # Initialize environment
         if self.env_type == "strategic_pricing":
             self.env = StrategicPricingMDP(self.dh, self.bins)
-            self.env.initialise_env()
 
-    def train_agent(self):
+    def train_agent(self,
+                    evaluate=False,
+                    n_eval_steps=10):
         """Train the agent for a chosen number of steps and episodes.
 
+        Args:
+            evaluate (bool): whether to evaluate agent
+            n_eval_steps (int): number of evaluation steps
         """
         # Fit the agent
-        self.agent.fit(self.num_episodes, self.num_episodes)
+        if not evaluate:
+            self.agent.fit(self.num_episodes, self.num_steps)
+        
+        self.agent_cumrewards = []
+        if evaluate:
+            self.build_evaluation()
+            for i in range(int(self.num_episodes/n_eval_steps)):
+                self.agent.fit(n_eval_steps, self.num_steps)
+                self.agent_cumrewards.append(self._evaluate_total_agent_reward())
+            self.hist_cumrewards = self._evaluate_total_hist_reward()
 
     def get_results(self):
         """Get the results of training.
@@ -106,19 +127,143 @@ def save_parameters(self):
         self.policy = self.agent.policy
         self.q_table = self.agent.q_table
 
-
-    def evaluate(self,
-                 state):
-        """Evaluate the learned policy at a particular state.
+    
+    def _inverse_scale_feature(self,
+                               values,
+                               labels):
+        """De-bin and de-normalize feature values.
 
         Args:
-            state: state for which an action needs to be predicted.
+            labels (list): list of feature labels
+            values (list): list of (scaled) feature values
+        
+        Returns:
+        """
+        i_values = []
+        for i, label in enumerate(labels):
+            try:
+                scaler = self.dh.minmax_scalars[label]
+            except:
+                ipdb.set_trace()
+            val = np.array([v[i] for v in values])
+            val = scaler.inverse_transform(
+                    val.reshape(-1, 1))
+            i_values.append(val)
+        # transpose and convert to list
+        i_values = np.concatenate(
+            [np.expand_dims(v,1) for v in i_values],
+            1).squeeze(-1).tolist()
+        return i_values
+
+    def build_evaluation(self):
+        """ Save data for evaluation. """
+        # Get test data from data handler
+        self._eval_states = self.dh.get_states(split='test').to_numpy().tolist()
+        self._eval_actions = self.dh.get_actions(split='test').to_numpy().tolist()
+        self._eval_rewards = self.dh.get_rewards(split='test').to_numpy().tolist()
+
+        # get state and action indexes
+        self._eval_state_dims = list(range(self.env.state_dim))
+        self._eval_action_dims = list(range(self.env.state_dim, 
+                                    self.env.state_dim+self.env.action_dim))
+        # Get the binned states
+        self._eval_b_states = self.env.bin_states(self._eval_states, idxs=self._eval_state_dims)
+
+    def _evaluate_total_agent_reward(self):
+        """ Calculate the total reward obtained on the evaluation states using
+            the agent's policy.
+        
         Returns:
-            action_reward: action and reward for a given state
+            cumreward (float): total (not scaled) cumulative reward
+        """
+        # Get actions corresponding to agent's learned policy
+        b_actions_agent = self.agent.predict_actions(self._eval_b_states)
+
+        # De-bin the recommended actions
+        actions_agent = self.env.debin_states(b_actions_agent, idxs=self._eval_action_dims)
 
-        TODO: ensure that here output is action with max q values (NO exploration)
+        # Get reward based on agent policy
+        rewards_agent = self.agent.predict_rewards(self._eval_b_states, b_actions_agent)
+        
+        # Inverse scale agent rewards
+        rewards_agent = self._inverse_scale_feature(rewards_agent,
+                                                    self.dh.reward_labels)
+
+        return np.sum(rewards_agent)
+    
+    def _evaluate_total_hist_reward(self):
+        """ Calculate the total reward obtained on the evaluation states using
+            the agent's policy.
+        
+        Returns:
+            cumreward (float): total (not scaled) cumulative based on historical data
         """
-        # Get both action & reward
-        action_reward = self.agent._epsilon_greedy_policy(state)
-        return action_reward
+        # Get the binned actions
+        b_actions =  self.env.bin_states(self._eval_actions, idxs=self._eval_action_dims)
+
+        # Get reward based on historical policy
+        rewards_hist = self.agent.predict_rewards(self._eval_b_states, b_actions)
+
+        # Inverse scale agent rewards
+        rewards_hist = self._inverse_scale_feature(rewards_hist,
+                                                    self.dh.reward_labels)
+
+        return np.sum(rewards_hist)
+
+    def evaluate_agent(self,
+                 epsilon=0):
+        """Evaluate the learned policy for the test states
+
+        Rewards are calculated using the average reward matrix.
+
+        Args:
+            epsilon: value of epsilon in the epsilon-greedy policy
+                (default= 0 corresponds to pure exploitation)
+        Returns:
+            states (list): list of test states
+            actions (list): list of test actions (historical)
+            rewards_hist (list): list of historical rewards (calculated)
+            actions_agent (list): list of recommended actions
+            rewards_agent (list): list of rewards obtained by agent (calculated)
+        """
+        # Get test data from data handler
+        states = self.dh.get_states(split='test').to_numpy().tolist()
+        actions = self.dh.get_actions(split='test').to_numpy().tolist()
+        rewards = self.dh.get_rewards(split='test').to_numpy().tolist()
+
+        # get state and action indexes
+        state_dims = list(range(self.env.state_dim))
+        action_dims = list(range(self.env.state_dim, 
+                                    self.env.state_dim+self.env.action_dim))
+        # Get the binned states
+        b_states = self.env.bin_states(states, idxs=state_dims)
+        # Inverse scaling
+        states = self._inverse_scale_feature(states, self.dh.state_labels)
+
+        # Get the binned actions
+        b_actions =  self.env.bin_states(actions, idxs=action_dims)
+
+        # Get actions corresponding to agent's learned policy
+        b_actions_agent = self.agent.predict_actions(b_states)
+
+        # De-bin the recommended actions
+        actions_agent = self.env.debin_states(b_actions_agent, idxs=action_dims)
+
+        # Get reward based on agent policy
+        rewards_agent = self.agent.predict_rewards(b_states, b_actions_agent)
+
+        # Get reward based on historical policy
+        rewards_hist = self.agent.predict_rewards(b_states, b_actions)
 
+        #  Apply inverse scaling to actions, states, and rewards
+        states = self._inverse_scale_feature(states,
+                                            self.dh.state_labels)
+        actions_hist = self._inverse_scale_feature(actions,
+                                                    self.dh.action_labels)
+        actions_agent = self._inverse_scale_feature(actions_agent,
+                                                    self.dh.action_labels)
+        rewards_hist = self._inverse_scale_feature(rewards_hist,
+                                                    self.dh.reward_labels)
+        rewards_agent = self._inverse_scale_feature(rewards_agent,
+                                                    self.dh.reward_labels)
+        return states, actions_hist, b_actions, rewards_hist, actions_agent, b_actions_agent, rewards_agent
diff --git a/src/foundation/environment.py b/src/foundation/environment.py
index f2686cb..1a42bf4 100644
--- a/src/foundation/environment.py
+++ b/src/foundation/environment.py
@@ -1,6 +1,8 @@
 # Import packages
+import ipdb as ipdb
 import sparse
 import numpy as np
+import ipdb
 
 from src.foundation.super_classes import MDP
 
@@ -19,7 +21,6 @@ def __init__(self, dh, bins=None):
 
         if bins is None:
             bins = [10]
-        self._average_rewards = None
         self.state_to_action = {}
 
         self._state_mdp_data = None
@@ -31,11 +32,13 @@ def __init__(self, dh, bins=None):
             self.bins = [10] * (self.state_dim + self.action_dim)
         else:
             self.bins = bins
-
+        
+        self.initialise_env()
+        
     def initialise_env(self):
         """Create the environment given the MDP information."""
         self._average_rewards = self._make_rewards_from_data()
-
+    
     def _transform_df_to_numpy(self):
         """Transform the MDP data from a dataframe to a numpy array
         """
@@ -53,7 +56,8 @@ def _join_state_action(self):
         for i in range(len(self._reward_mdp_data)):
             state_array = self._state_mdp_data[i].tolist()
             action_array = self._action_mdp_data[i].tolist()
-            zipped.append(state_array + action_array)
+            zipped.append(state_array+action_array)
+
         return zipped
 
     def _bin_state_action_space(self, zipped):
@@ -63,21 +67,54 @@ def _bin_state_action_space(self, zipped):
         Returns:
             np.array: Binned state-action pairs.
         """
-        binned = []
-        for i in range(len(zipped)):
-            binned.append(self._bin_state(zipped[i]))
-        return np.array(binned)
+        return np.array(self.bin_states(zipped))
+
+    def bin_states(self, states, idxs=None):
+        """ Bin a list of states.
+        Args:
+            states (list[list]): State to bin.
+            idxs (list): indexes of the state dimensions
+                This argument can be used if the state list contains
+                only certain features (e.g. only actions)
+        Returns:
+            b_states (list): Binned state
+        """
+        b_states = []
+        for state in states:
+            b_states.append(
+                self._bin_state(state, idxs=idxs)
+            )
+        return b_states
 
+    def debin_states(self, b_states, idxs=None):
+        """ Debin a list of binned states.
+        Args:
+            b_states (list[list]): Binned states to debin.
+            idxs (list): indexes of the state dimensions
+                This argument can be used if the state list contains
+                only certain features (e.g. only actions)
+        Returns:
+            states (list): Binned state
+        """
+        states = []
+        for b_state in b_states:
+            states.append(
+                self._debin_state(b_state, idxs=idxs)
+            )
+        return states
+        
     def _bin_state(self, state, idxs=None):
         """Bin a singular state.
+
         The states are binned according to the number of bins
-        of each feature.
+        of each feature. 
+
         Args:
             state (list): State to bin.
             idxs (list): indexes of the state dimensions
                 This argument can be used if the state list contains
                 only certain features (e.g. only actions)
-
+        
         Returns:
             binned (list): Binned state
         """
@@ -88,23 +125,51 @@ def _bin_state(self, state, idxs=None):
         for i, value in zip(idxs, state):
             binned.append(
                 np.digitize(
-                    value,
-                    [n / self.bins[i] if n < self.bins[i] else 1.01 \
-                     for n in range(1, self.bins[i] + 1)]
+                    value, 
+                    [n/self.bins[i] if n<self.bins[i] else 1.01 \
+                     for n in range(1,self.bins[i]+1)]
                 )
             )
         return binned
+    
+    def _debin_state(self, b_state, idxs=None):
+        """ Debin a singular states.
 
-    def _bin_state_old(self, state):
-        """Bin a singular state.
+        Returns middle point of the bin.
+        
+        Args:
+            b_state (list): Binned state to de-bin
+        """
+        if idxs == None:
+            idxs = range(len(state))
+
+        state = []
+        for i, value in zip(idxs, b_state):
+            # append middle point of the state bin
+            try:
+                state.append((value + 0.5) / self.bins[i])
+            except:
+                ipdb.set_trace()
+        return state
+
+    def _debin_state(self, b_state, idxs=None):
+        """ Debin a singular states.
+        Returns middle point of the bin.
 
         Args:
-            state (list): State to bin.
+            b_state (list): Binned state to de-bin
         """
-        binned = []
-        for i in range(len(state)):
-            binned.append(np.digitize(state[i], np.linspace(0, 1 + 1 / self.bins[i], self.bins[i])))
-        return binned
+        if idxs == None:
+            idxs = range(len(b_state))
+
+        state = []
+        for i, value in zip(idxs, b_state):
+            # append middle point of the state bin
+            try:
+                state.append((value + 0.5) / self.bins[i])
+            except:
+                ipdb.set_trace()
+        return state
 
     def _get_counts_and_rewards_per_bin(self, binned):
         """Create a dictionary of counts of datapoints per bin and sum the associated rewards.
@@ -150,7 +215,8 @@ def _create_average_reward_matrix(self, bins_dict):
             data.extend([value[1] / value[0]])
 
         coords = np.array(coords).T.tolist()
-        return sparse.COO(coords, data)
+        
+        return sparse.COO(coords, data, shape=tuple(self.bins))
 
     def _make_rewards_from_data(self):
         """Create sparse matrix of the state-action pairs and associated rewards from the inputted dataset.
@@ -168,7 +234,6 @@ def _make_rewards_from_data(self):
         binned = self._bin_state_action_space(zipped)
 
         bins_dict = self._get_counts_and_rewards_per_bin(binned)
-
         average_reward_matrix = self._create_average_reward_matrix(bins_dict)
 
         return average_reward_matrix
diff --git a/src/performance/performance_evaluator.py b/src/performance/performance_evaluator.py
index d3b665a..0e53a0e 100644
--- a/src/performance/performance_evaluator.py
+++ b/src/performance/performance_evaluator.py
@@ -9,10 +9,15 @@
 from src.data_handler.data_handler import DataHandler
 from src.explainability.pdp import PDP
 from datetime import datetime
+from main import run_all
+
 
 class PerformanceEvaluator:
     def __init__(self):
-        # Directory containing all results for this evaluator
+        """Initialise a PerformanceEvaluator.
+
+        """
+        # Define the directory containing all the evaluations of this performance evaluator
         self.init_time = time.strftime("%Y%m%d-%H:%M:%S")
         os.mkdir(f"evaluations/performance-{self.init_time}")
 
@@ -22,18 +27,32 @@ def __init__(self):
         self.NUM_SAMPLES = int(1e+5)
 
         # Graph colors
-        self.time_color = "purple"
-        self.space_color = "green"
+        self.time_color = "cornflowerblue"
+        self.space_color = "sienna"
+
+        # Print statements
+        self.verbose = True
+
+        # How many of the top lines of memory allocation to record
+        self.num_memalloc_lines_to_keep = 5000
 
     def get_all_performance_evaluations(self):
+        """Perform and save all desired performance evaluations."""
         self.get_benchmark_performance()
         self.get_performance_graphs()
-        self.per_function_time_breakdown()
+        self.get_time_breakdown_per_function()
+        self.get_space_breakdown_per_function()
 
     def get_benchmark_performance(self):
-        """ Get performance (space and time) for constant benchmark settings.
+        """Get performance (space and time) for constant benchmark settings.
+
+        Note, the reason the timeit module is not used is because the piece of code being profiled
+        is lengthy (whole training flow); timeit is designed to test small snippets of code
+        by running them many times.
 
         """
+        if self.verbose:
+            print("\nGETTING BENCHMARK PERFORMANCE")
         # Halt any ongoing memory tracing not to get memory results from previous code runs
         tracemalloc.stop()
 
@@ -52,10 +71,12 @@ def get_benchmark_performance(self):
         # Memory usage results returned in bytes (num. Bytes / 1024 = num. KiB ; num. KiB / 1024 = num. MiB)
         _, first_peak = tracemalloc.get_traced_memory()
 
+        tracemalloc.stop()
+
         # Peak in MiB
-        peak = first_peak/(1024*1024)
+        peak = first_peak / (1024 * 1024)
 
-        time_summary = f"Time spent (s): {end_time-start_time}"
+        time_summary = f"Time spent (s): {end_time - start_time}"
         space_summary = f"Peak memory usage (MiB): {peak}"
 
         with open(f"evaluations/performance-{self.init_time}/benchmark-report.txt", "w") as report_file:
@@ -63,47 +84,70 @@ def get_benchmark_performance(self):
                               f'Number of bins: {self.NUM_BINS}\nNumber of samples: {self.NUM_SAMPLES}\n\n'
                               f'RESULTS\n{time_summary}\n{space_summary}')
 
-    def pre_performance_run_config(self):
+    def do_pre_benchmark_run_configuration(self):
+        """Prepare the benchmark performance run."""
         tracemalloc.stop()
         tracemalloc.start()
         start_time = time.time()
         return start_time
 
-    def post_performance_run_results(self):
+    def get_post_benchmark_run_results(self):
+        """Get the results of the benchmark performance run."""
         end_time = time.time()
         _, first_peak = tracemalloc.get_traced_memory()
         peak = first_peak / (1024 * 1024)
         return end_time, peak
 
     def get_performance_graphs(self):
-        """ Plot performance (time and space) against chosen varying parameters:
+        """ Plot performance (time and space) against chosen varying parameters.
+
+            Parameters:
             - Number of samples
             - Number of episodes
             - Number of bins (held constant for all dimensions)
 
         """
+        if self.verbose:
+            print("\nGETTING PERFORMANCE GRAPHS")
+
+        if self.verbose:
+            print("* Plot of performance vs number of samples")
         # Plot of performance vs number of samples
-        num_sample_range = [int(1e1), int(1e2), int(1e3)]
+        num_sample_range = [int(1e2), int(1e3), int(1e4), int(1e5)]
         times, memory = self.get_times_and_memory_from_parameter_range(parameter_name="num_samples", x=num_sample_range)
-        self.plot_performance_graph(xlabel="samples", x=num_sample_range, times=times, memory=memory)
+        self.plot_performance_graph(x_label="samples", x=num_sample_range, times=times, memory=memory)
 
+        if self.verbose:
+            print("* Plot of performance vs number of episodes")
         # Plot of performance vs number of episodes
-        num_ep_range = [int(1e1), int(1e2), int(1e3)]
+        num_ep_range = [int(1e1), int(1e2), int(1e3), int(1e4)]
         times, memory = self.get_times_and_memory_from_parameter_range(parameter_name="num_episodes", x=num_ep_range)
-        self.plot_performance_graph(xlabel="episodes", x=num_ep_range, times=times, memory=memory)
+        self.plot_performance_graph(x_label="episodes", x=num_ep_range, times=times, memory=memory)
 
+        if self.verbose:
+            print("* Plot of performance vs number of bins")
         # Plot of performance vs number of bins
-        num_bin_range = [2, 5, 10, 20, 30, 40]
+        num_bin_range = [2, 5, 10, 20, 30, 40, 50]
         times, memory = self.get_times_and_memory_from_parameter_range(parameter_name="num_bins", x=num_bin_range)
-        self.plot_performance_graph(xlabel="bins", x=num_bin_range, times=times, memory=memory)
+        self.plot_performance_graph(x_label="bins", x=num_bin_range, times=times, memory=memory)
 
     def get_times_and_memory_from_parameter_range(self, parameter_name, x):
+        """Get time and memory complexities for varying values of an inputted parameter name.
+
+        Args:
+            parameter_name (str): Name of the parameter against which time and memory complexities are recorded.
+            x (list[int]): Range of values to vary the parameter.
+
+        Returns:
+            (list[float], list[float]): Time and memory recordings for the different parameter values in x.
+
+        """
         times = []
         memory = []
 
         for val in x:
 
-            start_time = self.pre_performance_run_config()
+            start_time = self.do_pre_benchmark_run_configuration()
 
             if parameter_name == "num_samples":
                 self.run_training_loop(num_episodes=self.NUM_EP, num_bins=self.NUM_BINS, num_samples=val)
@@ -114,29 +158,49 @@ def get_times_and_memory_from_parameter_range(self, parameter_name, x):
             elif parameter_name == "num_episodes":
                 self.run_training_loop(num_episodes=val, num_bins=self.NUM_BINS, num_samples=self.NUM_SAMPLES)
 
-            end_time, peak = self.post_performance_run_results()
+            end_time, peak = self.get_post_benchmark_run_results()
 
             times += [end_time - start_time]
             memory += [peak]
 
         return times, memory
 
-    def plot_performance_graph(self, xlabel, x, times, memory):
+    def plot_performance_graph(self, x_label, x, times, memory):
+        """Plot and save a performance graph.
+
+        Args:
+            x_label (str): Label of the horizontal axis (e.g. "episodes", "samples", or "bins").
+            x (list[int]): Range of the x parameter across which time and memory have been recorded.
+            times (list[int]): Results of time complexity recording for different values of x.
+            memory (list[int]): Results of memory complexity recording for different values of x.
+
+        """
+        plt.style.use("seaborn-v0_8-darkgrid")
 
         fig, ax1 = plt.subplots()
-        ax1.plot(x, times, color=self.time_color)
+        ax1.plot(x, times, color=self.time_color, alpha=0.6)
         ax2 = ax1.twinx()
-        ax2.plot(x, memory, color=self.space_color)
+        ax2.plot(x, memory, color=self.space_color, alpha=0.6)
 
         ax1.set_ylabel("Time (s)", color=self.time_color)
         ax2.set_ylabel("Space (MiB)", color=self.space_color)
+        plt.xlabel(f"Number of {x_label}")
+        plt.title(f"Time and space complexities vs number of {x_label}")
+        plt.grid()
+        plt.tight_layout()
+
+        plt.savefig(f"evaluations/performance-{self.init_time}/perf_vs_{x_label}.png")
 
-        plt.xlabel(f"Number of {xlabel}")
-        plt.savefig(f"evaluations/performance-{self.init_time}/perf_vs_{xlabel}.png")
+    def get_time_breakdown_per_function(self):
+        """Get a per-function breakdown of time complexity.
+
+            See: https://www.machinelearningplus.com/python/cprofile-how-to-profile-your-python-code/.
+            And: https://stackoverflow.com/questions/51536411/saving-cprofile-results-to-readable-external-file.
+
+        """
+        if self.verbose:
+            print("\nGETTING TIME BREAKDOWN PER FUNCTION")
 
-    def per_function_time_breakdown(self):
-        # See: https://www.machinelearningplus.com/python/cprofile-how-to-profile-your-python-code/
-        # And: https://stackoverflow.com/questions/51536411/saving-cprofile-results-to-readable-external-file
         profiler = cProfile.Profile()
         profiler.enable()
         self.run_training_loop(num_episodes=self.NUM_EP,
@@ -150,73 +214,60 @@ def per_function_time_breakdown(self):
         with open(f"evaluations/performance-{self.init_time}/per_function_time.txt", "w") as outfile:
             outfile.write(stream.getvalue())
 
+    def get_space_breakdown_per_function(self):
+        """Get a per-function breakdown of space complexity.
+
+        """
+        if self.verbose:
+            print("\nGETTING SPACE BREAKDOWN PER FUNCTION")
+
+        tracemalloc.start()
+
+        self.run_training_loop(num_episodes=self.NUM_EP,
+                               num_bins=self.NUM_BINS,
+                               num_samples=self.NUM_SAMPLES)
+
+        snapshot = tracemalloc.take_snapshot()
+        top_stats = snapshot.statistics('lineno')
+
+        with open(f"evaluations/performance-{self.init_time}/per_function_space.txt", "w") as outfile:
+            outfile.write(f"TOP {self.num_memalloc_lines_to_keep} MEMORY ALLOCATION LINES")
+            for stat in top_stats[:self.num_memalloc_lines_to_keep]:
+                outfile.write(f"\n{stat}")
+
+        tracemalloc.stop()
+
     def run_training_loop(self, num_episodes, num_bins, num_samples):
-        """ Run an example main.py.
+        """Run an example main.py.
+
+            Eventually, may load the hyperparameter data from a user-facing file.
 
-        Eventually, may load the hyperparameter data from a user-facing file.
+        Args:
+            num_episodes (int): Number of episodes in the training loop.
+            num_bins (int): Number of bins used for digitisation (equal number of bins for all dimensions).
+            num_samples (int): Number of datapoint samples used for training.
 
         """
+        if self.verbose:
+            print(f"-> Running training loop for {num_episodes} episodes, {num_bins} bins, {num_samples} samples")
 
-        # This is the function call to potentially be replaced
+        # Get the hyperparameter dictionary for the specified parameters
         hyperparam_dict = self.get_hyperparam_dict_ds_data(num_episodes, num_bins, num_samples)
 
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-        print(f"{timestamp}: Load data")
-        states = hyperparam_dict['states']
-        actions = hyperparam_dict['actions']
-        rewards = hyperparam_dict['rewards']
-        n_samples = hyperparam_dict['n_samples']
-        dh = DataHandler(data_path=hyperparam_dict['data_path'],
-                         state_labels=states,
-                         action_labels=actions,
-                         reward_labels=rewards,
-                         n_samples=n_samples)
-
-        # Preprocess the data
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-        print(f"{timestamp}: Preprocess data")
-        dh.prepare_data_for_engine(col_delimiter=hyperparam_dict['col_delimiter'],
-                                   cols_to_normalise=hyperparam_dict[
-                                       'cols_to_normalise'])
-
-        # Create engine
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-
-        print(f"{timestamp}: Initialize Engine")
-        engine = Engine(dh,
-                        agent_type=hyperparam_dict['agent_type'],
-                        env_type=hyperparam_dict['env_type'],
-                        num_episodes=hyperparam_dict['num_episodes'],
-                        num_steps=hyperparam_dict['num_steps'],
-                        bins=hyperparam_dict['bins']
-                        )
-        # Create world
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-        print(f"{timestamp}: Create the world")
-        engine.create_world()
-
-        # Train agent
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-        print(f"{timestamp}: Train the agent on {n_samples} samples")
-        engine.train_agent()
-
-        # Plot PDPs
-        timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
-        print(f"{timestamp}: Show PDPs plots")
-        pdp = PDP(bins=engine.env.bins,
-                  minmax_scalars=dh.minmax_scalars,
-                  action_labels=actions,
-                  state_labels=states)
-        pdp.build_data_for_plots(engine.agent.Q, engine.agent.Q_num_samples)
-        type_features = hyperparam_dict['feature_types']
-        fig_name = "PDP plots - All states"
-        pdp.plot_pdp(states_names=states, fig_name=fig_name,
-                     type_features=type_features, savefig=True, all_states=True)
-        fig_name = "PDP plots - Visited states"
-        pdp.plot_pdp(states_names=states, fig_name=fig_name,
-                     type_features=type_features, savefig=True, all_states=False)
+        run_all(hyperparam_dict, verbose=False, show_plots=False)
 
     def get_hyperparam_dict_ds_data(self, num_episodes, num_bins, num_samples):
+        """Load the hyperparameter dictionaries for the Datasparq data.
+
+        Args:
+            num_episodes (int): Number of episodes in the training loop.
+            num_bins (int): Number of bins used for digitisation (equal number of bins for all dimensions).
+            num_samples (int): Number of datapoint samples used for training.
+
+        Returns:
+            (dict): Hyperparameter dictionary specific to the Datasparq dataset.
+
+        """
         hyperparam_dict_ds_data = {
             'states': ['lead_time', 'length_of_stay',
                        'competitor_price_difference_bin', 'demand_bin'],
@@ -246,5 +297,7 @@ def get_hyperparam_dict_ds_data(self, num_episodes, num_bins, num_samples):
 
 if __name__ == "__main__":
     performance_evaluator = PerformanceEvaluator()
-    performance_evaluator.per_function_time_breakdown()
-    # performance_evaluator.get_all_performance_evaluations()
\ No newline at end of file
+    performance_evaluator.get_all_performance_evaluations()
+
+# TODO: remove all the print statements from the other classes (environment, agent, etc...) because it clouds the output
+#  here.


Test patch:
diff --git a/tests/evaluation.ipynb b/tests/evaluation.ipynb
new file mode 100644
index 0000000..c1a94ed
--- /dev/null
+++ b/tests/evaluation.ipynb
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.foundation.engine import Engine\n",
+    "from src.data_handler.data_handler import DataHandler\n",
+    "from src.explainability.pdp import PDP\n",
+    "from datetime import datetime\n",
+    "import ipdb"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define hyper-parameters for DS data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hyperparam_dict_ds_data = {\n",
+    "        'states': ['lead_time', 'length_of_stay',\n",
+    "                   'competitor_price_difference_bin', 'demand_bin'],\n",
+    "        'actions': ['price'],\n",
+    "        'bins': [10, 10, 4, 4, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.\n",
+    "        'rewards': ['reward'],\n",
+    "        'feature_types': {\n",
+    "            'lead_time': \"continuous\",\n",
+    "            'length_of_stay': \"continuous\",\n",
+    "            'competitor_price_difference_bin': \"discrete\",\n",
+    "            'demand_bin': \"discrete\",\n",
+    "            'price': \"continuous\",\n",
+    "            'reward': \"continuous\"\n",
+    "        },\n",
+    "        'n_samples': 100000,\n",
+    "        'data_path': 'data/ds-data/my_example_data.parquet',\n",
+    "        'col_delimiter': '|',\n",
+    "        'cols_to_normalise': ['lead_time', 'length_of_stay',\n",
+    "                   'competitor_price_difference_bin', 'demand_bin', 'price', 'reward'],\n",
+    "        'agent_type': 'q_learner',\n",
+    "        'env_type': 'strategic_pricing',\n",
+    "        'num_episodes': 100000,\n",
+    "        'num_steps': 1,\n",
+    "        'train_test_split': 0.2\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Load data\")\n",
+    "states = hyperparam_dict['states']\n",
+    "actions = hyperparam_dict['actions']\n",
+    "rewards = hyperparam_dict['rewards']\n",
+    "n_samples = hyperparam_dict['n_samples']\n",
+    "dh = DataHandler(data_path=hyperparam_dict['data_path'],\n",
+    "                    state_labels=states,\n",
+    "                    action_labels=actions,\n",
+    "                    reward_labels=rewards,\n",
+    "                    n_samples=n_samples)\n",
+    "\n",
+    "# Preprocess the data\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Preprocess data\")\n",
+    "dh.prepare_data_for_engine(col_delimiter=hyperparam_dict['col_delimiter'],\n",
+    "                            cols_to_normalise=hyperparam_dict[\n",
+    "                                'cols_to_normalise'])\n",
+    "\n",
+    "# Create engine\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "\n",
+    "print(f\"{timestamp}: Initialize Engine\")\n",
+    "engine = Engine(dh,\n",
+    "                agent_type=hyperparam_dict['agent_type'],\n",
+    "                env_type=hyperparam_dict['env_type'],\n",
+    "                num_episodes=hyperparam_dict['num_episodes'],\n",
+    "                num_steps=hyperparam_dict['num_steps'],\n",
+    "                bins=hyperparam_dict['bins'],\n",
+    "                train_test_split = hyperparam_dict['train_test_split']\n",
+    "                )\n",
+    "# Create world\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Create the world\")\n",
+    "engine.create_world()\n",
+    "\n",
+    "# Train agent\n",
+    "timestamp = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
+    "print(f\"{timestamp}: Train the agent on {n_samples} samples\")\n",
+    "engine.train_agent()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "    ###########################################################\n",
+    "    ################ Evaluate agent ###########################\n",
+    "    ###########################################################\n",
+    "\n",
+    "    # TODO: denorm states, actions and rewards (using datahandler's inverse scaling)\n",
+    "\n",
+    "    states, actions, rewards_hist, actions_agent, rewards_agent = \\\n",
+    "        engine.evaluate_agent()\n",
+    "    \n",
+    "    # Sum obtained reward optimal vs historical policy\n",
+    "    import numpy as np\n",
+    "    print(f\"Return based on historical data: {np.sum(rewards_hist)}\")\n",
+    "    print(f\"Return based on agent policy: {np.sum(rewards_agent)}\")\n",
+    "    \n",
+    "    import matplotlib.pyplot as plt\n",
+    "    plt.scatter(actions, actions_agent)\n",
+    "    plt.savefig('policy.png')\n",
+    "\n",
+    "    ipdb.set_trace()\n",
+    "\n",
+    "    ###########################################################\n",
+    "    ################# End of evaluation #######################\n",
+    "    ###########################################################\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tests/main.py b/tests/main.py
new file mode 100644
index 0000000..30fbaa7
--- /dev/null
+++ b/tests/main.py
@@ -0,0 +1,144 @@
+from src.foundation.engine import Engine
+from src.data_handler.data_handler import DataHandler
+from src.explainability.pdp import PDP
+from datetime import datetime
+import ipdb
+
+def policy_deviation():
+    pass
+
+def run_all(hyperparam_dict):
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    print(f"{timestamp}: Load data")
+    states = hyperparam_dict['states']
+    actions = hyperparam_dict['actions']
+    rewards = hyperparam_dict['rewards']
+    n_samples = hyperparam_dict['n_samples']
+    dh = DataHandler(data_path=hyperparam_dict['data_path'],
+                     state_labels=states,
+                     action_labels=actions,
+                     reward_labels=rewards,
+                     n_samples=n_samples)
+
+    # Preprocess the data
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    print(f"{timestamp}: Preprocess data")
+    dh.prepare_data_for_engine(col_delimiter=hyperparam_dict['col_delimiter'],
+                               cols_to_normalise=hyperparam_dict[
+                                   'cols_to_normalise'])
+
+    # Create engine
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+
+    print(f"{timestamp}: Initialize Engine")
+    engine = Engine(dh,
+                    agent_type=hyperparam_dict['agent_type'],
+                    env_type=hyperparam_dict['env_type'],
+                    num_episodes=hyperparam_dict['num_episodes'],
+                    num_steps=hyperparam_dict['num_steps'],
+                    bins=hyperparam_dict['bins'],
+                    train_test_split = hyperparam_dict['train_test_split']
+                    )
+    # Create world
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    print(f"{timestamp}: Create the world")
+    engine.create_world()
+
+    # Train agent
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    print(f"{timestamp}: Train the agent on {n_samples} samples")
+    engine.train_agent()
+
+    ###########################################################
+    ################ Evaluate agent ###########################
+    ###########################################################
+
+    # TODO: denorm states, actions and rewards (using datahandler's inverse scaling)
+
+    states, actions, rewards_hist, actions_agent, rewards_agent = \
+        engine.evaluate_agent()
+    ipdb.set_trace()
+    # Sum obtained reward optimal vs historical policy
+    import numpy as np
+    print(f"Return based on historical data: {np.sum(rewards_hist)}")
+    print(f"Return based on agent policy: {np.sum(rewards_agent)}")
+    
+    import matplotlib.pyplot as plt
+    plt.scatter(actions, actions_agent)
+    plt.savefig('policy.png')
+
+    ipdb.set_trace()
+
+    ###########################################################
+    ################# End of evaluation #######################
+    ###########################################################
+    
+    # Plot PDPs
+    timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    print(f"{timestamp}: Show PDPs plots")
+    pdp = PDP(bins=engine.env.bins,
+              minmax_scalars=dh.minmax_scalars,
+              action_labels=actions,
+              state_labels=states)
+    pdp.build_data_for_plots(engine.agent.Q, engine.agent.Q_num_samples)
+    type_features = hyperparam_dict['feature_types']
+    fig_name = "PDP plots - All states"
+    pdp.plot_pdp(states_names=states, fig_name=fig_name,
+                 type_features=type_features, savefig=True, all_states=True)
+    fig_name = "PDP plots - Visited states"
+    pdp.plot_pdp(states_names=states, fig_name=fig_name,
+                 type_features=type_features, savefig=True, all_states=False)
+
+
+if __name__ == "__main__":
+    hyperparam_dict_ds_data = {
+        'states': ['lead_time', 'length_of_stay',
+                   'competitor_price_difference_bin', 'demand_bin'],
+        'actions': ['price'],
+        'bins': [10, 10, 4, 4, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.
+        'rewards': ['reward'],
+        'feature_types': {
+            'lead_time': "continuous",
+            'length_of_stay': "continuous",
+            'competitor_price_difference_bin': "discrete",
+            'demand_bin': "discrete",
+            'price': "continuous",
+            'reward': "continuous"
+        },
+        'n_samples': 200000,
+        'data_path': 'data/ds-data/my_example_data.parquet',
+        'col_delimiter': '|',
+        'cols_to_normalise': ['lead_time', 'length_of_stay',
+                   'competitor_price_difference_bin', 'demand_bin', 'price', 'reward'],
+        'agent_type': 'q_learner',
+        'env_type': 'strategic_pricing',
+        'num_episodes': 100000,
+        'num_steps': 1,
+        'train_test_split': 0.2
+    }
+
+    hyperparam_dict_kaggle_data = {
+        'states': ['competitorPrice', 'adFlag', 'availability'],
+        'actions': ['price'],
+        'rewards': ['revenue'],
+        'feature_types': {
+            'competitorPrice': "continuous",
+            'adFlag': "discrete",
+            'availability': "discrete",
+            'price': "continuous",
+            'revenue': "continuous"
+        },
+        'bins': [10, 2, 2, 10], #TODO: these correspond to the states and actions. Probably should change to a dict.
+        'n_samples': 100,
+        'data_path': 'data/kaggle-dummy-dataset/train.csv',
+        'col_delimiter': '|',
+        'cols_to_normalise': ['competitorPrice', 'adFlag', 'availability', 'price'],
+        'agent_type': 'q_learner',
+        'env_type': 'strategic_pricing',
+        'num_episodes': 10,
+        'num_steps': 1,
+        'train_test_split': 0.2
+    }
+    for i in range(10):
+        run_all(hyperparam_dict_ds_data)
+        # ran this 10 times to check everything was fine.
diff --git a/tests/test_foundation/test_agent.py b/tests/test_foundation/test_agent.py
index b389d43..6ce1f8a 100644
--- a/tests/test_foundation/test_agent.py
+++ b/tests/test_foundation/test_agent.py
@@ -19,13 +19,10 @@ def setUpClass(cls) -> None:
         cls.dh.prepare_data_for_engine(col_delimiter=',',
                                         cols_to_normalise=states+actions)
 
-
     def setUp(self) -> None:
         self.env = StrategicPricingMDP(self.dh)
-        self.env.initialise_env()
         self.agent = QLearningAgent(self.env, gamma=0.9)
 
-
     def tearDown(self) -> None:
         del self.agent
 
diff --git a/tests/test_foundation/test_environment.py b/tests/test_foundation/test_environment.py
index 9a078d0..794be78 100644
--- a/tests/test_foundation/test_environment.py
+++ b/tests/test_foundation/test_environment.py
@@ -1,7 +1,138 @@
 import unittest
 
-class TestEngine(unittest.TestCase):
-    pass
+import pandas as pd
+
+from src.data_handler.data_handler import DataHandler
+from src.foundation.environment import StrategicPricingMDP
+import numpy as np
+import sparse
+
+
+class TestStrategicPricingMDP(unittest.TestCase):
+
+    dh = None
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        states = ['competitorPrice', 'adFlag', 'availability']
+        actions = ['price']
+        rewards = ['revenue']
+        n_samples = 50
+        cls.dh = DataHandler('tests/test_env_data.csv', states, actions, rewards,
+                              n_samples=n_samples)
+        cls.dh.prepare_data_for_engine(col_delimiter=',',
+                                        cols_to_normalise=states+actions)
+
+    def setUp(self) -> None:
+        self.env = StrategicPricingMDP(self.dh)
+
+    def tearDown(self) -> None:
+        del self.env
+
+    def test_transform_df_to_numpy(self):
+        target_states = self.dh.get_states().to_numpy()
+        target_actions = self.dh.get_actions().to_numpy()
+        target_rewards = self.dh.get_rewards().to_numpy()
+        self.env._transform_df_to_numpy()
+        result_states = self.env._state_mdp_data
+        result_actions = self.env._action_mdp_data
+        result_reward = self.env._reward_mdp_data
+        assert np.array_equal(target_states, result_states)
+        assert np.array_equal(target_actions, result_actions)
+        assert np.array_equal(target_rewards, result_reward)
+
+    def test_join_state_action(self):
+        self.env._state_mdp_data = np.array([[1, 2, 3], [4, 5, 6]])
+        self.env._action_mdp_data = np.array([[1], [2]])
+        self.env._reward_mdp_data = np.array([[1], [2]])
+        result = self.env._join_state_action()
+        target = np.array([[1, 2, 3, 1], [4, 5, 6, 2]])
+        assert np.array_equal(result, target)
+
+    def test_bin_state_action_space(self):
+        zipped = np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]])
+        self.env.bins = [10, 10, 5, 5]
+        result = self.env._bin_state_action_space(zipped)
+        target = np.array([[1, 2, 1, 2], [5, 6, 3, 4]])
+        assert np.array_equal(result, target)
+
+    def test_bin_states(self):
+        states = [[0.34, 0.19, 0.79], [0.35, 0.37, 0.44]]
+        self.env.bins = [10, 10, 5]
+        result = self.env.bin_states(states)
+        target = [[3, 1, 3], [3, 3, 2]]
+        assert result == target
+
+    def test_debin_states(self):
+        states = [[3, 1, 3], [3, 3, 2]]
+        self.env.bins = [10, 10, 5]
+        result = self.env.debin_states(states)
+        target = [[0.35, 0.15, 0.7], [0.35, 0.35, 0.5]]
+        assert result == target
+
+    def test_bin_state(self):
+        state = np.array([0.34, 0.19, 0.79])
+        self.env.bins = [10, 10, 5]
+        result = self.env._bin_state(state)
+        target = [3, 1, 3]
+        assert result == target
+
+    def test_debin_state(self):
+        state = np.array([3, 1, 3])
+        self.env.bins = [10, 10, 5]
+        result = self.env._debin_state(state)
+        target = [0.35, 0.15, 0.7]
+        assert result == target
+
+    def test_get_counts_and_rewards_per_bin(self):
+        binned = np.array([[1, 2, 3, 1], [1, 2, 3, 2], [1, 2, 3, 1], [1, 2, 3, 2]])
+        self.env._reward_mdp_data = np.array([[1], [2], [1], [2]])
+        result = self.env._get_counts_and_rewards_per_bin(binned)
+        target = {'1,2,3,1': [2, 2], '1,2,3,2': [2, 4]}
+        assert result == target
+
+    def test_create_average_reward_matrix(self):
+        counts_rewards = {'1,2,3,1': [2, 2], '1,2,3,2': [2, 4]}
+        result = self.env._create_average_reward_matrix(counts_rewards)
+        target = np.zeros((2, 3, 4, 3))
+        target[1, 2, 3, 1] = 1
+        target[1, 2, 3, 2] = 2
+        target = sparse.COO(target)
+        assert result == target
+
+    def test_make_rewards_from_data(self):
+        self.env._state_mdp_data = pd.DataFrame([[0.1, 0.23, 0.4], [0.5, 0.6, 0.7]])
+        self.env._action_mdp_data = pd.DataFrame([[0.1], [0.2]])
+        self.env._reward_mdp_data = pd.DataFrame([[1], [2]])
+        self.env.bins = [10, 10, 10, 10]
+        result = self.env._make_rewards_from_data()
+
+        target = np.zeros((10, 10, 10, 10))
+        target[1, 2, 3, 1] = 1
+        target[5, 6, 7, 2] = 2
+        target = sparse.COO(target)
+        assert result == target
+
+    def test_reset(self):
+        self.env._state_mdp_data = np.array([[0.1, 0.23, 0.4], [0.5, 0.6, 0.7]])
+        result = self.env.reset()
+        assert result in [[1, 2, 4], [5, 6, 7]]
+
+    def test_step(self):
+        self.env._state_mdp_data = pd.DataFrame(
+            [[0.1, 0.23, 0.4], [0.5, 0.6, 0.7]])
+        self.env._action_mdp_data = pd.DataFrame([[0.1], [0.2]])
+        self.env._reward_mdp_data = pd.DataFrame([[1], [2]])
+        self.env.bins = [10, 10, 10, 10]
+        self.env._make_rewards_from_data()
+        state = [1, 2, 3]
+        action = [1]
+        result = self.env.step(state, action)
+        assert result == (state, state, 1, True)
+
+
+
+