Unit1 of deep RL course

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 255.73 +/- 25.25
+      name: mean_reward
+      verified: false
+---
+
+# **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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f4d901c9900>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d901c9990>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d901c9a20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d901c9ab0>", "_build": "<function ActorCriticPolicy._build at 0x7f4d901c9b40>", "forward": "<function ActorCriticPolicy.forward at 0x7f4d901c9bd0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4d901c9c60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d901c9cf0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4d901c9d80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d901c9e10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d901c9ea0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d901c9f30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4d901638c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701364583370441840, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 252, "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, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:261d7216ea583188d0e79010bb932b80b45e2a43c4967a55efbbe9edfe4b7ca0
+size 148206

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f4d901c9900>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d901c9990>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d901c9a20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d901c9ab0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4d901c9b40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4d901c9bd0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4d901c9c60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d901c9cf0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4d901c9d80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d901c9e10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d901c9ea0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d901c9f30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4d901638c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1701364583370441840,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 252,
+    "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,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c99a9eac648cf704d80bba4851014104d4adc2266bf944852d7d84f94838b60
+size 88490

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a44c3ef44e5a06c5ed625f5484c39b576995a2ad9e57241eaa9a0bb5fcffaf9
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 255.72982279999997, "std_reward": 25.251086800205393, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-04T10:23:03.194481"}