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: 252.41 +/- 18.77
+      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 0x7ff545698c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff545698ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff545698d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff545698dc0>", "_build": "<function ActorCriticPolicy._build at 0x7ff545698e50>", "forward": "<function ActorCriticPolicy.forward at 0x7ff545698ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff545698f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff54561b040>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff54561b0d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff54561b160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff54561b1f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff54561b280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff545696960>"}, "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": 1675490516744814605, "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 sha256:7b1cc007130a49181afb19e3c2f335c7650822c54cbaae8aad4584edd1a1003c
+size 147424

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 0x7ff545698c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff545698ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff545698d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff545698dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff545698e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff545698ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff545698f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff54561b040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff54561b0d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff54561b160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff54561b1f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff54561b280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff545696960>"
+    },
+    "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": 1675490516744814605,
+    "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 sha256:2d0c965acc3658d92d35863cf5af764cb17caa8f73a8f20bbf2d2b40daa1987b
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71dda113756717bd64054680abe9aad501dba8ce43493f7f2125fbec46355771
+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": 252.40636293454372, "std_reward": 18.765218176671702, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-04T06:34:54.342664"}