sderev/PPO-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: 299.97 +/- 20.57
+      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 0x7f10d66a4c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f10d66a4ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f10d66a4d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f10d66a4dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f10d66a4e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f10d66a4ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f10d66a4f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f10d66a7040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f10d66a70d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f10d66a7160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f10d66a71f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f10d66a7280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f10d66a07e0>"}, "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": 10010624, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673604246212368688, "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": -0.0010623999999999079, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2444, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+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 sha256:792a489ec27169bab3b3553c310e225d162f4d732ca929c8e49a9bc331c6697c
+size 147296

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 0x7f10d66a4c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f10d66a4ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f10d66a4d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f10d66a4dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f10d66a4e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f10d66a4ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f10d66a4f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f10d66a7040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f10d66a70d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f10d66a7160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f10d66a71f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f10d66a7280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f10d66a07e0>"
+    },
+    "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": 10010624,
+    "_total_timesteps": 10000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1673604246212368688,
+    "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": -0.0010623999999999079,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 2444,
+    "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 sha256:427080d71c537d129e6157b8bf7eb18be7ee201d1a39ee7f733fd09afea841cb
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6931fb298d0fa35b6b18608afd27e20ee6226d14c1eb674478208b56a0e859fd
+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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 299.9703611784933, "std_reward": 20.565821670557746, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-13T12:44:02.294929"}