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: 266.40 +/- 22.88
+      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 0x7f020a005940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f020a0059d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f020a005a60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f020a005af0>", "_build": "<function ActorCriticPolicy._build at 0x7f020a005b80>", "forward": "<function ActorCriticPolicy.forward at 0x7f020a005c10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f020a005ca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f020a005d30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f020a005dc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f020a005e50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f020a005ee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f020a005f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f020a000810>"}, "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": 1674586977456648733, "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-5.10.147+-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 sha256:7bc2003db70a2f24418e2edadb559bf83746f98e158e2acc8f59d556a303adf4
+size 147536

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 0x7f020a005940>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f020a0059d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f020a005a60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f020a005af0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f020a005b80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f020a005c10>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f020a005ca0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f020a005d30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f020a005dc0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f020a005e50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f020a005ee0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f020a005f70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f020a000810>"
+    },
+    "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": 1674586977456648733,
+    "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-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b560dbeb6eb1d2780edbdd320757a9c9a0acc7923c9dde73455ab43821d8256
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65d5e66440ba5a0de31fb203919f87013f5471c11619bb07a2b13025d59f4218
+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": 266.3988315804448, "std_reward": 22.877371114748613, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-25T19:04:44.332701"}