Submitting Deep RL course unit 1 assignment

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 194.68 +/- 76.58
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f4d5fd5c7a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d5fd5c830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d5fd5c8c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d5fd5c950>", "_build": "<function ActorCriticPolicy._build at 0x7f4d5fd5c9e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4d5fd5ca70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d5fd5cb00>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4d5fd5cb90>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d5fd5cc20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d5fd5ccb0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d5fd5cd40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4d5fda3ba0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1660217314.4086797, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:655d3449a12c23ecdc4c50a6092c500cce3b735f25d1435b4b1066bc64cc7edf
+size 147148

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f4d5fd5c7a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d5fd5c830>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d5fd5c8c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d5fd5c950>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4d5fd5c9e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4d5fd5ca70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d5fd5cb00>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4d5fd5cb90>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d5fd5cc20>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d5fd5ccb0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d5fd5cd40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4d5fda3ba0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1660217314.4086797,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADPMZj6qfqE/Whu7Prujs765kso+MPECPgAAAAAAAAAAxmGavqjY1j1dk4k9iX0ZvgGkVb5AZYG/AAAAAAAAgD8A/uK8KcgJus64jLseR/i2glg1uq+nojoAAIA/AACAPzNrZ76Dux28S5xWu/8oA7mClYo9ombUOQAAgD8AAIA/2o3vPYUtmjpr4eu9ioSWvNmBnTwVsIO9AACAPwAAgD8aZ2A9KWAyul/axTqiHB41hjZSu6Nr5bkAAIA/AACAP7MKOT0fhaY4j56kvDvG9Dzq1AO7eQKAvAAAgD8AAIA/zaZNvFyfFDlIjdI8P5pWvNlnUruPcQS9AAAAAAAAAAAAONQ7H523uQw+hjem9uQyEBlEO5LJoLYAAIA/AACAP4Ytnr47sh8/OgDMPSc2Q74+oRE92lGbuwAAAAAAAAAAYB6lvqekTD8EQYW8IRspvh0shD3mZg68AAAAAAAAAACa+CA9rqG3upKJfLsJYcY7MBXxOUGNtjwAAIA/AACAP9rZ2D0KlzY6EkrtuQmKKbYGWAu83a0SOQAAgD8AAIA/M2+0uw4vqT8jZoy9sHa9vtXkgjx6zog9AAAAAAAAAAAdXaA+eYMjPq6I172+h32+OiP+PJaT7rwAAAAAAAAAAE181T0UbIC6OoPHvN6yFzYNqJc5nmSItQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d82d0eb85a1f69556ea2020df9ae4cdb4f44ce80381e93061cd63791c14ab68
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6cb0fcd19b91c3f50a554188278d85ded3e886de8442db7da896e2e61f283d48
+size 43201

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 194.68084171482207, "std_reward": 76.57786597379126, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-11T12:04:58.322515"}