Upload PPO CartPole-v1 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 500.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **CartPole-v1**
+This is a trained model of a **PPO** agent playing **CartPole-v1**
+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 0x7979df01fb50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7979df01fbe0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7979df01fc70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7979df01fd00>", "_build": "<function ActorCriticPolicy._build at 0x7979df01fd90>", "forward": "<function ActorCriticPolicy.forward at 0x7979df01fe20>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7979df01feb0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7979df01ff40>", "_predict": "<function ActorCriticPolicy._predict at 0x7979df024040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7979df0240d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7979df024160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7979df0241f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7979df01a200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693802136078153475, "learning_rate": 0.0003, "tensorboard_log": null, "_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": -0.1468799999999999, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 32, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True]", "bounded_above": "[ True  True  True  True]", "_shape": [4], "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]", "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]", "low_repr": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]", "high_repr": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAgAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "2", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-CartPole-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c1e1091103fdb70dbbd63b22d54662992b3e5651a1668f5c01599f86ada2329
+size 138409

ppo-CartPole-v1/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-CartPole-v1/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7979df01fb50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7979df01fbe0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7979df01fc70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7979df01fd00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7979df01fd90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7979df01fe20>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7979df01feb0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7979df01ff40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7979df024040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7979df0240d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7979df024160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7979df0241f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7979df01a200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 114688,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1693802136078153475,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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": -0.1468799999999999,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 32,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_shape": [
+            4
+        ],
+        "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
+        "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
+        "low_repr": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
+        "high_repr": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAgAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "2",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-CartPole-v1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f326ab4de655d50304d9cbb2eb5acd3a406d3d7af1bae3e486abab54cea49c64
+size 82809

ppo-CartPole-v1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98c038f917d80fa7e6b1e617dfd635882fb69f87b9d3698814161c18bf893337
+size 40769

ppo-CartPole-v1/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-CartPole-v1/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 500.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-04T04:40:27.617536"}