PPO model trained on LunarLander-v2

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: 241.28 +/- 29.62
+      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 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 0x7f5de16c0c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5de16c0ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5de16c0d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5de16c0dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f5de16c0e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f5de16c0ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5de16c0f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5de16c4040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5de16c40d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5de16c4160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5de16c41f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5de16be480>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672597690312915768, "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": 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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c9c0d7dc6bed6cd540eb3bb8dd974db22a1b4619fa77e1ffd87d5443fdec579
+size 147214

ppo-LunarLander-v1/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v1/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 0x7f5de16c0c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5de16c0ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5de16c0d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5de16c0dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5de16c0e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5de16c0ee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5de16c0f70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5de16c4040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5de16c40d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5de16c4160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5de16c41f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5de16be480>"
+    },
+    "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": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1672597690312915768,
+    "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": 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-v1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f56fa48155ff5b74eae7a0b74da7d09afeac73c9adf0d550a35e89c0ef43ec39
+size 87929

ppo-LunarLander-v1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5a64311a1361623e173eb27a38e9df11150d524b2aed8ad6932b4cb25fa4236
+size 43201

ppo-LunarLander-v1/pytorch_variables.pth

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

ppo-LunarLander-v1/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 241.27628258229907, "std_reward": 29.624714773838342, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-01T19:05:53.116454"}