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: 297.39 +/- 23.61
+      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 0x78960d1d9000>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78960d1d9090>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78960d1d9120>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78960d1d91b0>", "_build": "<function ActorCriticPolicy._build at 0x78960d1d9240>", "forward": "<function ActorCriticPolicy.forward at 0x78960d1d92d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78960d1d9360>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78960d1d93f0>", "_predict": "<function ActorCriticPolicy._predict at 0x78960d1d9480>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78960d1d9510>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78960d1d95a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78960d1d9630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78960d379680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 368000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652272866.0520153, "learning_rate": 0.0, "tensorboard_log": "logs", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.67232, "_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": 19344, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "low_repr": "-inf", "high_repr": "inf", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76e71e37d84fa48d0c86996226c7130af9119cb8a40f6d5c2681bda170e76536
+size 149251

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x78960d1d9000>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78960d1d9090>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78960d1d9120>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78960d1d91b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x78960d1d9240>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78960d1d92d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78960d1d9360>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78960d1d93f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78960d1d9480>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78960d1d9510>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78960d1d95a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78960d1d9630>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78960d379680>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 368000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652272866.0520153,
+    "learning_rate": 0.0,
+    "tensorboard_log": "logs",
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.67232,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIfqg0YibmckCUhpRSlIwBbJRLpowBdJRHQHUGU/W1+iJ1fZQoaAZoCWgPQwj0o+GU+eZzQJSGlFKUaBVLs2gWR0B1B2FK02LpdX2UKGgGaAloD0MI5wDBHL1WckCUhpRSlGgVS7JoFkdAdQeE6T4cm3V9lChoBmgJaA9DCKbVkLhHanBAlIaUUpRoFUufaBZHQHUHz4xk/bF1fZQoaAZoCWgPQwisAN9tHkVzQJSGlFKUaBVLpmgWR0B1B/T7VJ+VdX2UKGgGaAloD0MIxjTTvU6DckCUhpRSlGgVS7hoFkdAdQgtwJgLJHV9lChoBmgJaA9DCEfoZ+o1pnJAlIaUUpRoFUu2aBZHQHUIUvboKUp1fZQoaAZoCWgPQwghdTv7CrJyQJSGlFKUaBVLiWgWR0B1pImBvrGBdX2UKGgGaAloD0MIforjwCu5cECUhpRSlGgVS6FoFkdAdaWfb9If83V9lChoBmgJaA9DCPLSTWIQhHJAlIaUUpRoFUuYaBZHQHWnOC9RJmN1fZQoaAZoCWgPQwgsnKT544JzQJSGlFKUaBVLt2gWR0B1qCwUxmCidX2UKGgGaAloD0MISiTRy+idcUCUhpRSlGgVS6hoFkdAdah1OCXhO3V9lChoBmgJaA9DCGSuDKpNcHNAlIaUUpRoFUu+aBZHQHWpQUg0TDh1fZQoaAZoCWgPQwjkLy3qk85xQJSGlFKUaBVLk2gWR0B1qcsMAmzCdX2UKGgGaAloD0MIqFMe3cjucUCUhpRSlGgVS4NoFkdAdaopeu3c6HV9lChoBmgJaA9DCBsuck/XeHRAlIaUUpRoFUukaBZHQHWrLDQ7cO91fZQoaAZoCWgPQwh+calKW0lyQJSGlFKUaBVLqGgWR0B1q1iLEUCadX2UKGgGaAloD0MITDeJQWBWckCUhpRSlGgVS59oFkdAdatSS/0ulHV9lChoBmgJaA9DCHPxtz3Bc3FAlIaUUpRoFUuUaBZHQHWsSYb83uN1fZQoaAZoCWgPQwiNRdPZyRVyQJSGlFKUaBVLkmgWR0B1rM57w8W9dX2UKGgGaAloD0MItp+M8WF4c0CUhpRSlGgVS5hoFkdAdazHbh3qzXV9lChoBmgJaA9DCPUsCOX9cHBAlIaUUpRoFUubaBZHQHWs8+iaiK11fZQoaAZoCWgPQwgiwyreyKVxQJSGlFKUaBVLqmgWR0B1raJrLyMDdX2UKGgGaAloD0MILo7KTRQ0ckCUhpRSlGgVS6loFkdAda4VlPJq7HV9lChoBmgJaA9DCJMCC2CKYXJAlIaUUpRoFUuRaBZHQHWvae05U991fZQoaAZoCWgPQwjirl5FBuxzQJSGlFKUaBVLqWgWR0B1r8j7hvR7dX2UKGgGaAloD0MIoaNVLWmRc0CUhpRSlGgVS7doFkdAdbAgrH2h7HV9lChoBmgJaA9DCMNHxJRIOHJAlIaUUpRoFUu1aBZHQHWw74SHuZ11fZQoaAZoCWgPQwiQ3QVKisJyQJSGlFKUaBVLt2gWR0B1sh7v5P/JdX2UKGgGaAloD0MITrnCu1zdcECUhpRSlGgVS6ZoFkdAdbIeZXuE3HV9lChoBmgJaA9DCBIT1PAtKnFAlIaUUpRoFUueaBZHQHWy9s7+1jR1fZQoaAZoCWgPQwjqIoWyMP5wQJSGlFKUaBVLjGgWR0B1tAgJTl1bdX2UKGgGaAloD0MI8s8M4kOWcUCUhpRSlGgVS5JoFkdAdbRq7iADrHV9lChoBmgJaA9DCBv1EI3u0XFAlIaUUpRoFUuVaBZHQHW07Ddgv111fZQoaAZoCWgPQwjb3m5JDuxzQJSGlFKUaBVLrGgWR0B1tj8k2P1ddX2UKGgGaAloD0MIKCob1hRFckCUhpRSlGgVS6RoFkdAdbZkjX4CZHV9lChoBmgJaA9DCPRNmgZFG3RAlIaUUpRoFUu+aBZHQHW2rlmvnr91fZQoaAZoCWgPQwid1Jel3VtzQJSGlFKUaBVLm2gWR0B1tybCrLhadX2UKGgGaAloD0MI409UNuzIcUCUhpRSlGgVS6VoFkdAdbc9/jKgZnV9lChoBmgJaA9DCAYOaOnKq3NAlIaUUpRoFUu3aBZHQHW4dKZlWfd1fZQoaAZoCWgPQwhjC0EOikBxQJSGlFKUaBVLomgWR0B1uQsvqTr3dX2UKGgGaAloD0MIZCR7hNrSckCUhpRSlGgVS55oFkdAdbrh7E5yVHV9lChoBmgJaA9DCGzp0VQPRnJAlIaUUpRoFUuoaBZHQHW7QyAQQMB1fZQoaAZoCWgPQwgMsmX5+pRyQJSGlFKUaBVLl2gWR0B1u4pjMFEBdX2UKGgGaAloD0MIV81zRH44ckCUhpRSlGgVS6hoFkdAdb3gmZ3LWHV9lChoBmgJaA9DCJc3h2v1f3NAlIaUUpRoFUuiaBZHQHW+LQgLZzx1fZQoaAZoCWgPQwiu78NBwnlxQJSGlFKUaBVLn2gWR0B1vverMkhSdX2UKGgGaAloD0MIYKxvYDKncUCUhpRSlGgVS5xoFkdAdb8cqvvBrXV9lChoBmgJaA9DCDHqWnvfyHNAlIaUUpRoFUu9aBZHQHW/tITXarZ1fZQoaAZoCWgPQwgT7pV562ZwQJSGlFKUaBVLmmgWR0B1v9Gus90SdX2UKGgGaAloD0MI2iCTjJw3ckCUhpRSlGgVS6ZoFkdAdcBeD3/PxHV9lChoBmgJaA9DCE6YMJpVdnRAlIaUUpRoFUu1aBZHQHXAs6V+qip1fZQoaAZoCWgPQwhMb38umvpzQJSGlFKUaBVLzWgWR0B1wM/4ZdfLdX2UKGgGaAloD0MIjLrW3mf3cUCUhpRSlGgVS5FoFkdAdcEIDYAbQ3V9lChoBmgJaA9DCOay0Tk/EHJAlIaUUpRoFUuPaBZHQHXB6PGQ0XR1fZQoaAZoCWgPQwiCxHb3wIFyQJSGlFKUaBVLv2gWR0B1wckzGgjAdX2UKGgGaAloD0MIe4MvTGYPcUCUhpRSlGgVS59oFkdAdcIz41xbS3V9lChoBmgJaA9DCJuuJ7quY3NAlIaUUpRoFUuraBZHQHXCPJNj9XN1fZQoaAZoCWgPQwgOoyB4fHJzQJSGlFKUaBVLvWgWR0B1wwIfKZDzdX2UKGgGaAloD0MIwVJdwMudcECUhpRSlGgVS5xoFkdAdcRftQbdanV9lChoBmgJaA9DCGCwG7at8XFAlIaUUpRoFUuWaBZHQHXEhs67ulZ1fZQoaAZoCWgPQwhOYaWCCmRyQJSGlFKUaBVLr2gWR0B1xH0L+glGdX2UKGgGaAloD0MI73TniaclckCUhpRSlGgVS6doFkdAdcWHO8kD6nV9lChoBmgJaA9DCNIag07IBXBAlIaUUpRoFUuaaBZHQHXGo0Mw1zh1fZQoaAZoCWgPQwgqjgOv1vxyQJSGlFKUaBVLmWgWR0B1x63pfQa8dX2UKGgGaAloD0MIiujX1g+tckCUhpRSlGgVS65oFkdAdchh11W8y3V9lChoBmgJaA9DCJyHE5jOfXBAlIaUUpRoFUuyaBZHQHXIhPCVKPJ1fZQoaAZoCWgPQwjfUPhs3S5wQJSGlFKUaBVLkmgWR0B1yRSl3yI6dX2UKGgGaAloD0MIjdXm/1UkdECUhpRSlGgVS7VoFkdAdcsqPfbblHV9lChoBmgJaA9DCL/zixK0MnJAlIaUUpRoFUuyaBZHQHXLHcDbJwN1fZQoaAZoCWgPQwg+esN9pPBzQJSGlFKUaBVLsWgWR0B1y3jR2KVIdX2UKGgGaAloD0MI/3ivWtleckCUhpRSlGgVS7NoFkdAdcvNR3u/lHV9lChoBmgJaA9DCI+K/ztiTnNAlIaUUpRoFUuyaBZHQHXNuzD4xlB1fZQoaAZoCWgPQwjSOqqaoJZyQJSGlFKUaBVLo2gWR0B1zm+zt1IRdX2UKGgGaAloD0MIpP56hQXlc0CUhpRSlGgVS6xoFkdAdc7fjjrAxnV9lChoBmgJaA9DCIJ1HD/Un29AlIaUUpRoFUuNaBZHQHXPQU+LWI51fZQoaAZoCWgPQwjbTIV4pDt0QJSGlFKUaBVLrWgWR0B1z2VW0Z3tdX2UKGgGaAloD0MIo3kAi7xGcECUhpRSlGgVS6BoFkdAddC8YQ8OkXV9lChoBmgJaA9DCIS7s3bb3nJAlIaUUpRoFUuHaBZHQHXRTY/Vy3l1fZQoaAZoCWgPQwiU93E0B45xQJSGlFKUaBVLm2gWR0B10aPNmlImdX2UKGgGaAloD0MIgGYQH5jDckCUhpRSlGgVS5VoFkdAddHsBhhH9XV9lChoBmgJaA9DCOi7W1mi/nJAlIaUUpRoFUu4aBZHQHXTQgcLjPx1fZQoaAZoCWgPQwi5N79h4gZwQJSGlFKUaBVLm2gWR0B11Bl9Sde6dX2UKGgGaAloD0MITFEujd9Mc0CUhpRSlGgVS6hoFkdAddRgx8D0UXV9lChoBmgJaA9DCOD2BIlt2HJAlIaUUpRoFUutaBZHQHXUpk9U0el1fZQoaAZoCWgPQwhtqBjn75tvQJSGlFKUaBVLsmgWR0B11OjBVMmGdX2UKGgGaAloD0MIy59vC9Z1ckCUhpRSlGgVS7loFkdAddUakRBeHHV9lChoBmgJaA9DCFJIMqv3yXFAlIaUUpRoFUupaBZHQHXVecQRPGh1fZQoaAZoCWgPQwiun/6z5lVxQJSGlFKUaBVLwGgWR0B11V/mT1TSdX2UKGgGaAloD0MIoYZvYd0pcECUhpRSlGgVS5RoFkdAddXqxC6YmnV9lChoBmgJaA9DCAlszsEzInJAlIaUUpRoFUuraBZHQHXW68xsVL11fZQoaAZoCWgPQwheEJGaNptwQJSGlFKUaBVLmmgWR0B118Od5IH1dX2UKGgGaAloD0MIMWE0K1sVdECUhpRSlGgVS75oFkdAddg/YraufXV9lChoBmgJaA9DCMzUJHhDvk9AlIaUUpRoFUt0aBZHQHXZpv5xiod1fZQoaAZoCWgPQwj7PEZ55plzQJSGlFKUaBVLuGgWR0B12ew8nuzAdX2UKGgGaAloD0MIBWnGoilLcUCUhpRSlGgVS5hoFkdAddrF/QSi/XV9lChoBmgJaA9DCF71gHkIXXFAlIaUUpRoFUuwaBZHQHXbsjzI3it1fZQoaAZoCWgPQwjlX8srF4lyQJSGlFKUaBVLnGgWR0B12+KEWZZ0dX2UKGgGaAloD0MI6L0xBIBHckCUhpRSlGgVS61oFkdAddx05EMLGHV9lChoBmgJaA9DCMP0vYagTHNAlIaUUpRoFUvRaBZHQHXdMKkVN6B1ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 19344,
+    "n_steps": 2048,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 256,
+    "n_epochs": 8,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "low_repr": "-inf",
+        "high_repr": "inf",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 32,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08db214fdb91fbc57fee7a9c362e2762d9243030692c3cf580495adaec2709c1
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24489a2d29338c1deb7c03d47326dfb89ab2b458a82ea8a5b7f514ccf34b14ec
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 297.3873606000001, "std_reward": 23.611415341602136, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-07T08:24:52.091722"}