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 0x7f5514f870a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5514f87130>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5514f871c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5514f87250>", "_build": "<function ActorCriticPolicy._build at 0x7f5514f872e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5514f87370>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5514f87400>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5514f87490>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5514f87520>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5514f875b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5514f87640>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5514f876d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5514f73480>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685270202748282271, "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": -0.015808000000000044, "_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": 248, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "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:ad1712db8bf157d245df4c01dc2b2f9b705edcd09f4c8e162c36b64bf1e1bb7f
+size 138443

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 0x7f5514f870a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5514f87130>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5514f871c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5514f87250>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5514f872e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5514f87370>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5514f87400>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5514f87490>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5514f87520>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5514f875b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5514f87640>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5514f876d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f5514f73480>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1685270202748282271,
+    "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": -0.015808000000000044,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQH9AAAAAAACMAWyUTfQBjAF0lEdAda1gWJrLyXV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHWt2iYb83x1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1revMbFS9dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAda4p6yB063V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHWvORLbpNd1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1r2k690zTdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbBT9sJpnHV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHWwl63RXwN1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1sNKODJ2ddX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbJfBeokzHV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHWyw/s3Q2N1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1s2WeHzpYdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbP0tAcDKnV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHW0UIomXw91fZQoaAZHQH9AAAAAAABoB030AWgIR0B1tg6U7jkudX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbkltj0+T3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHW5oODrZ8N1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1uiOzY287dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbo1WbPQfXV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHW6dT5wfhd1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1u4VO9FnadX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdbu1D0Dlo3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHW8obsF+ux1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1vO4XoC+2dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdb0n/kvK2nV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHW+tMbm2b51fZQoaAZHQH9AAAAAAABoB030AWgIR0B1vwb961LKdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdb+b6guh9XV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHXAGfkFOfx1fZQoaAZHQH9AAAAAAABoB030AWgIR0B1wHSE12q2dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdcJRXOnl4nV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHXFPTodMkB1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2Av/7zkIYdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdgNxk/bCanV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYDgrH2h7F1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2A7557gKndX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdgS9FF2FFnV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYE7OE/Spl1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2Bct+TeO5dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdgYJng5zYHV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYGPboKUml1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2B7mq5sj3dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdggOMVDa5HV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYIqO1fE4x1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2CSZLIxQBdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdgmDQ7cO9XV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYLSuuA7Pp1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2DkIt16mgdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdg6nZkCmuXV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYPGM4tHx11fZQoaAZHQH9AAAAAAABoB030AWgIR0B2DyorFwT/dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdg9mY0EX+HV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYQYHX2/SJ1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2EI/+sHSndX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdhFrv9cbBHV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYRqo/A0sR1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2EdzbN8mbdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdhNW5paibnV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYTrbxmTTx1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2FEFFDv3KdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdhS+r2g3+HV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYVFgH/tIF1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2Fs8lolD4dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdhoNXYDkl3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHYafI0ZWJd1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2GwOtnwocdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdlkWH1vl2nV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZZVNUOuq51fZQoaAZHQH9AAAAAAABoB030AWgIR0B2WlhYvFm4dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdlqIPsiSq3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZbZa3Zwn91fZQoaAZHQH9AAAAAAABoB030AWgIR0B2W7kmx+rmdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdlv0I1LrX3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZdkrCm/Fl1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2XedVea8ZdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdl6DLr5ZbXV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZfAT7EYO51fZQoaAZHQH9AAAAAAABoB030AWgIR0B2X1y925hCdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmEXO4XoDHV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZkEUKzAvd1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2ZHULDye7dX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmTtQKrq+3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZk/rWy1NR1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2ZUAksz2wdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmZJrcj7h3V9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZmeU+s5n11fZQoaAZHQH9AAAAAAABoB030AWgIR0B2Z1suWa+fdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmefK6nR9nV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZn0cn3L3d1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2aUlZ5iVjdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmmhOP/7znV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZqQOJ+Dvp1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2arzCk43ndX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdmsUdq+JxnV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZs5xR2r4p1fZQoaAZHQH9AAAAAAABoB030AWgIR0B2cADRtxdZdX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdnBwPRRdhXV9lChoBkdAf0AAAAAAAGgHTfQBaAhHQHZw6gmJFb51fZQoaAZHQH9AAAAAAABoB030AWgIR0B2cPuUliSadX2UKGgGR0B/QAAAAAAAaAdN9AFoCEdAdnE5GBnSOXVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVEAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAABAQEBlGgHjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKUjA1ib3VuZGVkX2Fib3ZllGgQKJYEAAAAAAAAAAEBAQGUaBRLBIWUaBh0lFKUjAZfc2hhcGWUSwSFlIwDbG93lGgQKJYQAAAAAAAAAJqZmcD//3//UHfWvv//f/+UaApLBIWUaBh0lFKUjARoaWdolGgQKJYQAAAAAAAAAJqZmUD//39/UHfWPv//f3+UaApLBIWUaBh0lFKUjAhsb3dfcmVwcpSMPVstNC44MDAwMDAyZSswMCAtMy40MDI4MjM1ZSszOCAtNC4xODg3OTAzZS0wMSAtMy40MDI4MjM1ZSszOF2UjAloaWdoX3JlcHKUjDlbNC44MDAwMDAyZSswMCAzLjQwMjgyMzVlKzM4IDQuMTg4NzkwM2UtMDEgMy40MDI4MjM1ZSszOF2UjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:cc109fe6cdf60026f490fa82e77fbee03047f9da4ae90ad648cff64167c96341
+size 82809

ppo-CartPole-v1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:974c5499cba31b5f262ac7c01520ca19c2615d7a451d7e10ce95e6c3a7b185a1
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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": "2023-05-28T10:43:06.964915"}