unit 1 model of deep rl course

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 168.17 +/- 105.80
+      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
+  

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 0x7f14ce083560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f14ce0835f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f14ce083680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f14ce083710>", "_build": "<function ActorCriticPolicy._build at 0x7f14ce0837a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f14ce083830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f14ce0838c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f14ce083950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f14ce0839e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f14ce083a70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f14ce083b00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f14ce0d55d0>"}, "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": 1651772532.1855714, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-lunar.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:953351593b0a05c859c7dc01d119f9c388286fab7ee85b06587fedc7ffa57eb6
+size 144024

ppo-lunar.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fd29f10869ccc6366c06ee856f3b459d909f51a89c609366ff08a38bfe730a2
+size 144024

ppo-lunar/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-lunar/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 0x7f14ce083560>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f14ce0835f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f14ce083680>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f14ce083710>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f14ce0837a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f14ce083830>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f14ce0838c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f14ce083950>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f14ce0839e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f14ce083a70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f14ce083b00>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f14ce0d55d0>"
+    },
+    "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": 1651772532.1855714,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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-lunar/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:403057a88586845720af9d7738963ee1f239bcab13ea9839f47a6ac6d6da96ae
+size 84829

ppo-lunar/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81baf7d0c8476134e40beb4f31072a0b1b0768a6c3d926c9e01f89914fb71b9a
+size 43201

ppo-lunar/pytorch_variables.pth

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

ppo-lunar/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: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55159d55de45e2f9f3ccbcabe8018da4067c9a572d55868cc9b95dcc62757034
+size 239790

results.json

@@ -0,0 +1 @@
+{"mean_reward": 168.1652144599781, "std_reward": 105.80149625856514, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T18:00:30.916716"}