Upload PPO LunarLander-v2 trained agent

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: 255.23 +/- 21.72
+      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 0x7f3a03df7250>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3a03df72e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3a03df7370>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3a03df7400>", "_build": "<function ActorCriticPolicy._build at 0x7f3a03df7490>", "forward": "<function ActorCriticPolicy.forward at 0x7f3a03df7520>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3a03df75b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3a03df7640>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3a03df76d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3a03df7760>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3a03df77f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3a03df7880>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3a64ac97c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683148077403347807, "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, "_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": 255, "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 '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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e57de2fac113f09a4b73e5e5c50f2bf6b6e0f74eac922bdcaf376bfeb9619ba2
+size 147536

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

ppo-LunarLander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f3a03df7250>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3a03df72e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3a03df7370>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3a03df7400>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3a03df7490>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3a03df7520>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3a03df75b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3a03df7640>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3a03df76d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3a03df7760>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3a03df77f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3a03df7880>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f3a64ac97c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1683148077403347807,
+    "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,
+    "_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": 255,
+    "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 '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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68cdc720687cc188a0117be0375828c1c9f8229e78b0b648d5f4830ce532639f
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb401bd83b1e043828e42d75d5b1e0f17897f7cda6e562f4fd2785622dd87b8f
+size 43329

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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 255.23342027356335, "std_reward": 21.719079028641936, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-03T21:33:23.636137"}