deep rl hf course unit1 ppo lunarlander

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: 263.24 +/- 16.09
+      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 0x7fbdae1b8ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbdae1b8d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbdae1b8dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbdae1b8e50>", "_build": "<function ActorCriticPolicy._build at 0x7fbdae1b8ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbdae1b8f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbdae1bd040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbdae1bd0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbdae1bd160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbdae1bd1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbdae1bd280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbdae1bd310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbdae1b64e0>"}, "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": 1, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676300662215405305, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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": 248, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:93864d2f3f811aca9ff4b4c4cb532602681411d35c1b0f513eef47eee93f58e4
+size 146620

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,92 @@
+{
+    "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 0x7fbdae1b8ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbdae1b8d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbdae1b8dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbdae1b8e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbdae1b8ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbdae1b8f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbdae1bd040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbdae1bd0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbdae1bd160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbdae1bd1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbdae1bd280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbdae1bd310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fbdae1b64e0>"
+    },
+    "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": 1,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676300662215405305,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_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": 248,
+    "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 sha256:81e6fe71158dcbefa6f972d8e8862785a1222f86200c8d5d5824a059e74663a6
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:272c02bfaa8a0e3247fca8245f3a2e378d0616d8033b321c9a01026dbda23ebe
+size 43393

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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 263.24364970335034, "std_reward": 16.093639346802632, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-13T15:39:37.511457"}