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: 157.11 +/- 38.17
+      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 0x7f31541d91f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31541d9280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31541d9310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31541d93a0>", "_build": "<function ActorCriticPolicy._build at 0x7f31541d9430>", "forward": "<function ActorCriticPolicy.forward at 0x7f31541d94c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31541d9550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31541d95e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f31541d9670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31541d9700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31541d9790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31541d9820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f31541d4810>"}, "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": 1677825036658596451, "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": 155, "n_steps": 2048, "gamma": 0.98, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 5, "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": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03efd6fcdc6fca9ba4ec8ad19c1b32eb7868719edfeddeb55ef27e1b7ea4d6a1
+size 147419

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f31541d91f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31541d9280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31541d9310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31541d93a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f31541d9430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f31541d94c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31541d9550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31541d95e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f31541d9670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31541d9700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31541d9790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31541d9820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f31541d4810>"
+    },
+    "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": 1677825036658596451,
+    "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": 155,
+    "n_steps": 2048,
+    "gamma": 0.98,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 5,
+    "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 sha256:742df5fbf78ddb634522e88bd27bd09956e5db9867421dbcddba5936e77580da
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f3cedc8bd7cb60dcc221a1b0c7abf0ba4e3907d4507808d0fda56951ed3177a
+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: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 157.10709218649714, "std_reward": 38.1728574675478, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-03T06:54:53.293658"}