best trial of a Lunalander model

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: 283.20 +/- 17.85
+      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
+
+...
+```

best_ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b7f9650671c4ee9e6f97546c995e9c0989a64b9aeedd2f2d005d2fc43a9d4ed
+size 147521

best_ppo-LunarLander-v2/_stable_baselines3_version

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

best_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 0x7f4f50195090>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f50195120>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f501951b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f50195240>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4f501952d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4f50195360>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f501953f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f50195480>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4f50195510>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f501955a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f50195630>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f501956c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4f5018eb80>"
+    },
+    "verbose": 0,
+    "policy_kwargs": {},
+    "num_timesteps": 2015232,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1706578728523506215,
+    "learning_rate": 0.0014101284211610063,
+    "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": -0.007616000000000067,
+    "_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": 492,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_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": 16,
+    "n_steps": 1024,
+    "gamma": 0.9998639676089875,
+    "gae_lambda": 0.972754819571804,
+    "ent_coef": 0.0115628100153596,
+    "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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"
+    }
+}

best_ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf94a2d78175679ebaeaf79f7509e8d34735eae6f4252c75935158a4823cb825
+size 87978

best_ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef8be44d639cb7a041816841811e2dff72c0902977e364299b1597b8ce892abe
+size 43634

best_ppo-LunarLander-v2/pytorch_variables.pth

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

best_ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,8 @@
+- OS: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Thu Oct 5 21:02:42 UTC 2023
+- Python: 3.10.13
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.2+cu121
+- GPU Enabled: False
+- Numpy: 1.26.3
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.28.1

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 0x7f4f50195090>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f50195120>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f501951b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f50195240>", "_build": "<function ActorCriticPolicy._build at 0x7f4f501952d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4f50195360>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f501953f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f50195480>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4f50195510>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f501955a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f50195630>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f501956c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4f5018eb80>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1706578728523506215, "learning_rate": 0.0014101284211610063, "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": -0.007616000000000067, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHBomVE/jbWMAWyUTRABjAF0lEdAhbMuQZGayHV9lChoBkdAcmiVymygPGgHTeIBaAhHQIWzZKL876p1fZQoaAZHQHMLOmelKsdoB0vnaAhHQIWzsH8jzI51fZQoaAZHQFHsE6T4cm1oB0vRaAhHQIW0JUcXFcZ1fZQoaAZHQHEC4yO7xutoB0vlaAhHQIW0M7wKBup1fZQoaAZHQHEL6gqVhThoB007AWgIR0CFtSzposZpdX2UKGgGR0BzwMZm7J4jaAdNSwFoCEdAhbWGdy1eB3V9lChoBkdAct5fe1rqMWgHTQYCaAhHQIW2rf+CK791fZQoaAZHQHKA8QumJnBoB00HAWgIR0CFuKjAzpHJdX2UKGgGR0BwK90yP+4taAdLxGgIR0CFuPXhfjS5dX2UKGgGR0ByfTwb2lEaaAdNBwFoCEdAhbkAOSW7e3V9lChoBkdAc0XQ4CIUJ2gHTR4BaAhHQIW5PoFFDv51fZQoaAZHQHHi8+7lJYloB02TAWgIR0CFuVUnXumadX2UKGgGR0Bz3wQOFxn4aAdL7WgIR0CFupPTodMkdX2UKGgGR0BxzglTm4iHaAdL2WgIR0CFuyrwvxpddX2UKGgGR0BzrmdoWYWtaAdNJgFoCEdAhbtmFi8WbnV9lChoBkdAcASPiDM/yGgHTRUBaAhHQIW7ypkwvg51fZQoaAZHQHIRm1pj+aVoB01lAWgIR0CFvO3zcynDdX2UKGgGR0ByBxE7W/ahaAdN2AFoCEdAhb97k4m1IHV9lChoBkdAcQhr/82rGWgHTdcBaAhHQIXAJw++ueV1fZQoaAZHQG/pvO6d1+1oB00KAWgIR0CFwSv/R3NcdX2UKGgGR0BznKhpQDV6aAdNvQJoCEdAhcE9APd2xXV9lChoBkdAb9s7Sy+pO2gHTZEBaAhHQIXBPf642CN1fZQoaAZHQHM27UkOZstoB0v5aAhHQIXDsXFcY651fZQoaAZHQHFdqMzdk8RoB01pAWgIR0CFxBbTtsvadX2UKGgGR0BwBImVqveQaAdNMAFoCEdAhcQpw84ginV9lChoBkdAczmNj9XLeWgHTYsBaAhHQIXE6KNyYHB1fZQoaAZHQHOkVGXokiVoB001AWgIR0CFxPlEJBw/dX2UKGgGR0ByRp51Ng0CaAdNTAFoCEdAhcXtYKYzBXV9lChoBkdAZiccurZJ1GgHTegDaAhHQIXGUa6z3RJ1fZQoaAZHQG/Up6hQFcJoB00gAWgIR0CFyP7Kq4pddX2UKGgGR0Bx5jYbsF+vaAdNEAFoCEdAhckkJjUd73V9lChoBkdAct9ZiNKh+WgHTRoCaAhHQIXJ9PnB+F11fZQoaAZHQHDLXLFGXoloB00TAWgIR0CFyml8gIQfdX2UKGgGR0Bx2ixgRbr1aAdNHgFoCEdAhcqtdiUgS3V9lChoBkdAcKXCyhSLqGgHTSsBaAhHQIXLFLDhtLt1fZQoaAZHQHBqyqdYnv5oB0vfaAhHQIXLVzwMH8l1fZQoaAZHQHNWJ1q33HtoB00NAWgIR0CFzIxubZvldX2UKGgGR0BxddtALRa5aAdNCgFoCEdAhc043WFvh3V9lChoBkdAcMjJ3PiT+2gHTQ8CaAhHQIXNpBcAzYV1fZQoaAZHQG7QZ9E1EVpoB00XAWgIR0CFzmRr8BMjdX2UKGgGR0BwVjPv8ZUDaAdNFQFoCEdAhc628qWkanV9lChoBkdAczkr/bTMJWgHTVoBaAhHQIXPUtGus911fZQoaAZHQHD4H1BdD6ZoB0vkaAhHQIXPi26TW5J1fZQoaAZHQHB8aXv6TGJoB0v4aAhHQIXQIYBNmDl1fZQoaAZHQHCTJzYEnstoB00BAWgIR0CF38RlHz6KdX2UKGgGR0Bv1a8pTdcjaAdNAQFoCEdAheDDJU5uInV9lChoBkdAcQbrS3LFGWgHS8BoCEdAheGNgjQiRnV9lChoBkdAcZg+3Ytg8mgHTTsBaAhHQIXhuEdvKlp1fZQoaAZHQHOD943WFvhoB00ZAWgIR0CF4bj5sTFmdX2UKGgGR0Bvi7AHmig1aAdNeQNoCEdAheKW606YFHV9lChoBkdAYqh/echC+mgHTegDaAhHQIXjIIhQm/p1fZQoaAZHQHNgA8W9DhNoB01sAWgIR0CF40hhYvFndX2UKGgGR0ByIuAqd6LPaAdNKQFoCEdAheNVsUIsy3V9lChoBkdAceBnvlU6xWgHS/hoCEdAhePBtUGVzXV9lChoBkdAc1ObAUL2H2gHS/doCEdAheSXwTdtVXV9lChoBkdAcrtHHmzSkWgHTRkBaAhHQIXk3pOerdZ1fZQoaAZHQHFB+PikwexoB0vJaAhHQIXlDWsijcp1fZQoaAZHQHIHaNlyzX1oB00IAWgIR0CF5T4AS39adX2UKGgGR0By1b7aZhKEaAdNqQJoCEdAheXSIHkcTHV9lChoBkdAcVQO1fE4vWgHS9NoCEdAhebhAnlXBHV9lChoBkdAbgeVu76HkGgHS/JoCEdAheb2PtD2J3V9lChoBkdAcCygCwKSgWgHTTsBaAhHQIXnGD8Lrop1fZQoaAZHQHGWCDM/yG1oB0vXaAhHQIXnI7cO9WZ1fZQoaAZHQG6bsCtA9mpoB0vUaAhHQIXoSkwevIR1fZQoaAZHQHBSVCXyAhBoB00RAWgIR0CF6XoWYWtVdX2UKGgGR0BwYNT850bMaAdL/GgIR0CF6Zxx1gYxdX2UKGgGR0BzKZJqZc9oaAdL2GgIR0CF6oPBi1ArdX2UKGgGR0ByFpJCjUNKaAdNWQFoCEdAheqwxWT5f3V9lChoBkdAbveetCAtnWgHS/RoCEdAhevEy+HrQnV9lChoBkdAb1sipNsWPGgHTToBaAhHQIXsGkep4r11fZQoaAZHQG/SVq33HrBoB01QAWgIR0CF7DGCqZMMdX2UKGgGR0BvDrPrv9cbaAdLwGgIR0CF7HEHdGiIdX2UKGgGR0BxHqBDohZAaAdNHAFoCEdAheyvdl/YrnV9lChoBkdAcnkzMRpUP2gHTS4BaAhHQIXssAJb+tN1fZQoaAZHQHKlC+tbLU1oB0vlaAhHQIXtMBp5/sp1fZQoaAZHQHGMQJswco9oB0vxaAhHQIXtae2/i5x1fZQoaAZHQHIgHQQcxTNoB00uAWgIR0CF7eKcd5prdX2UKGgGR0BvNFrTH80laAdLy2gIR0CF7wf9P1tgdX2UKGgGR0Bv10Nrj5sTaAdL/WgIR0CF8E1O0svqdX2UKGgGR0ByJ/YK6WgOaAdL82gIR0CF8PlYEGJOdX2UKGgGR0BwvGFIuoP1aAdNHwFoCEdAhfKYLkS26XV9lChoBkdAc3/jo6jnFGgHS/ZoCEdAhfLTkQwsXnV9lChoBkdAcWU4lyBClmgHTQkBaAhHQIXzkOqebut1fZQoaAZHQHGmASWZ7XxoB0vtaAhHQIXz/114gRt1fZQoaAZHQG7+ej/MnqpoB0v0aAhHQIX0iLZSNwR1fZQoaAZHQHHwBdpqREFoB00jAWgIR0CF9LschkiEdX2UKGgGR0BwFNkUbkwOaAdNOwFoCEdAhfXFhgE2YXV9lChoBkdAcYjCTUy57WgHTTwBaAhHQIX1znJT2nN1fZQoaAZHQHK/KwljVhFoB011AWgIR0CF9n3zMA3ldX2UKGgGR0Bwy0x+KCQLaAdLwmgIR0CF9xaNdZ7pdX2UKGgGR0BjREc0cfeUaAdN6ANoCEdAhfdoEr5IpnV9lChoBkdAcPzsLORkmWgHTRYBaAhHQIX3hLqUu+R1fZQoaAZHQHGPCF0xM39oB01FAWgIR0CF94XnhbW3dX2UKGgGR0ByU3hBJI1+aAdL8mgIR0CF97z1bqyGdX2UKGgGR0ByFlDOTq0MaAdLw2gIR0CF+O2kzoECdX2UKGgGR0ByLUxzq8lHaAdL6WgIR0CF+SJ8fFJhdX2UKGgGR0ByQ3lkpZwGaAdLwmgIR0CF+ZchTwUhdX2UKGgGR0BTotTtLL6laAdL1GgIR0CF+aEK3NLUdX2UKGgGR0BvzhIpYs/ZaAdL5GgIR0CF+pbblA/tdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 492, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_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": 16, "n_steps": 1024, "gamma": 0.9998639676089875, "gae_lambda": 0.972754819571804, "ent_coef": 0.0115628100153596, "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-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Thu Oct 5 21:02:42 UTC 2023", "Python": "3.10.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.2+cu121", "GPU Enabled": "False", "Numpy": "1.26.3", "Cloudpickle": "3.0.0", "Gymnasium": "0.28.1"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 283.1974506, "std_reward": 17.849028208894055, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-30T02:09:17.277790"}