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: 255.63 +/- 18.66
+      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 0x7f0603524f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0603529040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f06035290d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0603529160>", "_build": "<function ActorCriticPolicy._build at 0x7f06035291f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f0603529280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0603529310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f06035293a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0603529430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f06035294c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0603529550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f06035295e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f0603528800>"}, "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": 1680816509069444286, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABplmT3sWZ+5i/PcO0bamTo0OvO5e2uGOwAAAAAAAAAA8wdKvgWZFj6DNio+D7FDvsAUqzxtMZE8AAAAAAAAAADNS3y99mpHvE9jQTya95E8STKwPU7wbr0AAIA/AACAPzPx072H71c/7vtzvUzxxb5r7IO9L7MXvAAAAAAAAAAAjb8rvkoDnD8V3cG+I70Qv4g4Rr6AIri9AAAAAAAAAAC2/78+I657P1sD9jr82ay+8x1cPtpYwL0AAAAAAAAAAJqUr7xNQhM+/zq6PAT5jr47RSs7dCUHvAAAAAAAAAAAQLURPotdoz/Veeo+HtWrviamMT58MRY+AAAAAAAAAACaUS+9toMXvKAZcz2fzwY9eM+EvfYV2z0AAIA/AACAP6Y1lD24JsW562QsO6x7GjYHccQ6Ye4iNQAAAAAAAIA/ukYXvnPw7z5L+Kc9n59nvkrkKL28rcQ9AAAAAAAAAACacgy9uI+nu861a7u1MnA8SP5AvHqb+rwAAIA/AACAP5osqz0YdRk/WsRKvnMrob7nJQO9TaD0vQAAAAAAAAAAEyJlvicNEL0cUgI7ICOlOUAEfj5F5i+6AACAPwAAgD/mSao9Tt1FP9H3jrwlnpO+fIQyPTA37boAAAAAAAAAAIZEfD538CS9/HWgur0pYDmzcY6+ytLaOQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "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 sha256:97e1a46924a2384e81e40db0af93a7481de449fa0a6ac3abe0eecc9ac5d388b0
+size 147400

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 0x7f0603524f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0603529040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f06035290d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0603529160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f06035291f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0603529280>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0603529310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f06035293a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0603529430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f06035294c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0603529550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f06035295e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f0603528800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1680816509069444286,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAABplmT3sWZ+5i/PcO0bamTo0OvO5e2uGOwAAAAAAAAAA8wdKvgWZFj6DNio+D7FDvsAUqzxtMZE8AAAAAAAAAADNS3y99mpHvE9jQTya95E8STKwPU7wbr0AAIA/AACAPzPx072H71c/7vtzvUzxxb5r7IO9L7MXvAAAAAAAAAAAjb8rvkoDnD8V3cG+I70Qv4g4Rr6AIri9AAAAAAAAAAC2/78+I657P1sD9jr82ay+8x1cPtpYwL0AAAAAAAAAAJqUr7xNQhM+/zq6PAT5jr47RSs7dCUHvAAAAAAAAAAAQLURPotdoz/Veeo+HtWrviamMT58MRY+AAAAAAAAAACaUS+9toMXvKAZcz2fzwY9eM+EvfYV2z0AAIA/AACAP6Y1lD24JsW562QsO6x7GjYHccQ6Ye4iNQAAAAAAAIA/ukYXvnPw7z5L+Kc9n59nvkrkKL28rcQ9AAAAAAAAAACacgy9uI+nu861a7u1MnA8SP5AvHqb+rwAAIA/AACAP5osqz0YdRk/WsRKvnMrob7nJQO9TaD0vQAAAAAAAAAAEyJlvicNEL0cUgI7ICOlOUAEfj5F5i+6AACAPwAAgD/mSao9Tt1FP9H3jrwlnpO+fIQyPTA37boAAAAAAAAAAIZEfD538CS9/HWgur0pYDmzcY6+ytLaOQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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 sha256:631c73be6858b3c6f702400916e2ddfbeb5f22c0eb9bc3fb49ee58831fb68e68
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6c2a4c08e788009a454d9cb017fd01318ea0d5def1d9b39c22d867eef9287ce
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 255.62512401356162, "std_reward": 18.663076605743505, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-06T22:02:07.798622"}