ppo-lander_initial_commit

Maly_ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7bf282b526d89d1a02344ef6ad8cea0d127f3af1d27f4ccc471648a34685048
+size 148086

Maly_ppo-LunarLander-v2/_stable_baselines3_version

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

Maly_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 0x7ae21a5a2440>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ae21a5a24d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ae21a5a2560>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ae21a5a25f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ae21a5a2680>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ae21a5a2710>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ae21a5a27a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ae21a5a2830>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ae21a5a28c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ae21a5a2950>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ae21a5a29e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ae21a5a2a70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ae21a52e1c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1727034891746555220,
+    "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:": "gAWVQwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGdF7GNrCWOMAWyUTegDjAF0lEdAkvRsfA9FF3V9lChoBkdAY5wC8OCoTGgHTegDaAhHQJL6olLOAy51fZQoaAZHQGUUqjzqbBpoB03oA2gIR0CS/SMd92HMdX2UKGgGR0BmaHJgb6xgaAdN6ANoCEdAkv+Gbwz+FXV9lChoBkdAY/86V+qioWgHTegDaAhHQJMC+RwIdEN1fZQoaAZHQGd1xu0kWyloB03oA2gIR0CTBFyBTXJ6dX2UKGgGR0Bih+uPmxMWaAdN6ANoCEdAkwcJ5zHS4XV9lChoBkdAYfYqLCN0eWgHTegDaAhHQJMMhFb3XZp1fZQoaAZHQGGLGx+rlvJoB03oA2gIR0CTDxAsTWXkdX2UKGgGR0BGIvzvqkdnaAdL4mgIR0CTEZ8MNMGpdX2UKGgGR0Bjsy5Gz8gqaAdN6ANoCEdAkxJvxx1gY3V9lChoBkdAYKAxj8UEgWgHTegDaAhHQJMUdNcnmaJ1fZQoaAZHQGWdDfek56toB03oA2gIR0CTKXHO8kD7dX2UKGgGR0BjcDpkf9xZaAdN6ANoCEdAky/T3yqdYnV9lChoBkdAYpKksz2vjmgHTegDaAhHQJMxrNMXaal1fZQoaAZHQGNVEl/pdKNoB03oA2gIR0CTPOdZJTVEdX2UKGgGR0Bivs21lXijaAdN6ANoCEdAk0CGn4wh4nV9lChoBkdAYmmbe/Ho5mgHTegDaAhHQJNDbcafjCJ1fZQoaAZHQGUFYHgP3BZoB03oA2gIR0CTSeOCXhOydX2UKGgGR0BijVrylN1yaAdN6ANoCEdAk0yBVuJk5XV9lChoBkdAYlD5MURFqmgHTegDaAhHQJNPEKiO/+N1fZQoaAZHQGRW1nEl3QloB03oA2gIR0CTU+8zAN5MdX2UKGgGR0BfaubRWtEHaAdN6ANoCEdAk1okPczqKXV9lChoBkdAZXl6hxo7FWgHTegDaAhHQJNguWSlnAZ1fZQoaAZHQGKAAaFVT75oB03oA2gIR0CTY4vnKW9ldX2UKGgGR0Biis8PnSv1aAdN6ANoCEdAk2Yg88s+V3V9lChoBkdAZAJv9cbBGmgHTegDaAhHQJNm8g2ZRbd1fZQoaAZHQGEmcB2fTThoB03oA2gIR0CTaQPdl/YrdX2UKGgGR0BkTF36hxo7aAdN6ANoCEdAk2oYOpbUw3V9lChoBkdAYr69OARTTGgHTegDaAhHQJOBoIrvsqt1fZQoaAZHQGJOWeHzpX9oB03oA2gIR0CTg3AnDziCdX2UKGgGR0BmNIod+5OKaAdN6ANoCEdAk5Dtc8kleHV9lChoBkdAY7xDHfdhzGgHTegDaAhHQJOUFHFxXGR1fZQoaAZHQGMs5GjKxLVoB03oA2gIR0CTlpl/pdKNdX2UKGgGR0BmYGJzkp7UaAdN6ANoCEdAk5wB3mmtQ3V9lChoBkdAXp3/kvK2a2gHTegDaAhHQJOePykKu0V1fZQoaAZHQGNbyCnP3SNoB03oA2gIR0CToD0OmR/3dX2UKGgGR0Bny1ea8YhuaAdN6ANoCEdAk6M7Vz6rNnV9lChoBkdAZY2p4rz5GmgHTegDaAhHQJOnDGza9K51fZQoaAZHQGOQSeI2wV1oB03oA2gIR0CTrHghr30xdX2UKGgGR0Boq2RV6u4gaAdN6ANoCEdAk670vGp++nV9lChoBkdAZQB5BTn7pGgHTegDaAhHQJOxUx8D0UZ1fZQoaAZHQGN6VYp2EChoB03oA2gIR0CTsiKnNxEOdX2UKGgGR0BgjzLt/nW8aAdN6ANoCEdAk7QYRujynXV9lChoBkdAYuJEMspXqGgHTegDaAhHQJO1EfcN6Pd1fZQoaAZHQGR2CItUXHloB03oA2gIR0CTz1wA2hqTdX2UKGgGR0Bncoh2W6bwaAdN6ANoCEdAk9E4jSofjnV9lChoBkdALqlE7W/ag2gHTQkBaAhHQJPTVu+AVfx1fZQoaAZHQGGzlEy+HrRoB03oA2gIR0CT26DPWxyGdX2UKGgGR0BiGO+K0lZ6aAdN6ANoCEdAk960haC+UXV9lChoBkdAZFo/i5uqFWgHTegDaAhHQJPhH0Fr2xp1fZQoaAZHQGWEbW/ag29oB03oA2gIR0CT5nLvkRzzdX2UKGgGR0BmegDgZTAGaAdN6ANoCEdAk+izS5RTCXV9lChoBkdAYxjt/nW8RWgHTegDaAhHQJPrpWGRFJB1fZQoaAZHQGOShIe5nUVoB03oA2gIR0CT76l2eQMhdX2UKGgGR0BjbmXkYGdJaAdN6ANoCEdAk/SEfDDTB3V9lChoBkdAYyWFyJbdJ2gHTegDaAhHQJP5A9hZyMl1fZQoaAZHQGQVGUGFBY5oB03oA2gIR0CT+yF2FFlTdX2UKGgGR0BkZyHh0hePaAdN6ANoCEdAk/3eqJdjXnV9lChoBkdAZJjEy+HrQmgHTegDaAhHQJP/uP7vXsh1fZQoaAZHQGS9olMRHwxoB03oA2gIR0CUALFIuoP1dX2UKGgGR0ByYAppeu3daAdN5wFoCEdAlAINVaOghHV9lChoBkdAZqxzWf9P12gHTegDaAhHQJQXupgkTpR1fZQoaAZHQGHORdQfp2VoB03oA2gIR0CUGWg5R0lrdX2UKGgGR0BkbyzC1qnFaAdN6ANoCEdAlBtjJEH+qHV9lChoBkdAZyu8IRh+fGgHTegDaAhHQJQlgD5j6N51fZQoaAZHQGMUCbtqpLpoB03oA2gIR0CUKYN7BwdbdX2UKGgGR0Bjbf642CNCaAdN6ANoCEdAlCwOYIBzWHV9lChoBkdAbhSrUb1h9mgHTS8CaAhHQJQvcYFaB7N1fZQoaAZHQGWYrWRRuTBoB03oA2gIR0CUM4OGTLW7dX2UKGgGR0Bh4vV09yLiaAdN6ANoCEdAlDWGMwUQCnV9lChoBkdAZMP3NcGC7WgHTegDaAhHQJQ4fdj5Kvp1fZQoaAZHQF5mbF0gbIdoB03oA2gIR0CUPB0T101ZdX2UKGgGR0ByXeC8OCoTaAdNaQFoCEdAlD5rItDlYHV9lChoBkdAZg5bah6By2gHTegDaAhHQJRAb/YJ3Pl1fZQoaAZHQGLGfZdv865oB03oA2gIR0CUQlgmJFb3dX2UKGgGR0BgfsahpQDWaAdN6ANoCEdAlEaFk1/DtXV9lChoBkdAZu0uq3mV7mgHTegDaAhHQJRHeMkyDZl1fZQoaAZHQGOz6SDAaehoB03oA2gIR0CUSMe/pMYedX2UKGgGR0BxDDiOvMbFaAdNywFoCEdAlEswaR6ni3V9lChoBkdAcb8YqoZQ52gHTXsDaAhHQJRLedCmdiF1fZQoaAZHQGGhg6dUbUBoB03oA2gIR0CUYQzJp35fdX2UKGgGR0Blle9+PRzBaAdN6ANoCEdAlGJ3ocJdB3V9lChoBkdAcXrZQ53kgmgHTbwBaAhHQJRqo88s+V11fZQoaAZHQGh4Jxeb/fhoB03oA2gIR0CUaxoHLRrrdX2UKGgGR0BlRBiXpnpTaAdN6ANoCEdAlG348lolEHV9lChoBkdAcJ3IeYD1XmgHTX0BaAhHQJRxnYsd1dR1fZQoaAZHQGgmzundfsxoB03oA2gIR0CUc9cwQDmsdX2UKGgGR0BkRnrUsnRcaAdN6ANoCEdAlHhV+EytWHV9lChoBkdAZRUsuFpPAWgHTegDaAhHQJR94Q5FPSF1fZQoaAZHQHDBZmyxA0NoB02IA2gIR0CUf1pFkQPJdX2UKGgGR0BwDTO/tY0VaAdNfQFoCEdAlIBCIk7fYXV9lChoBkdAZi7+w1R+B2gHTegDaAhHQJSBnZJ04ip1fZQoaAZHQGsKap5u63BoB027AmgIR0CUg1ORkmQbdX2UKGgGR0ByiWzMRpUQaAdNUANoCEdAlIYE3XI2fnV9lChoBkdAZ+Xt65XlsGgHTegDaAhHQJSHO1IAfdR1fZQoaAZHQHCz20Z3s5ZoB02RAWgIR0CUkJ0GeMAFdX2UKGgGR0BkJQIBzV+aaAdN6ANoCEdAlJF3TZxrBXV9lChoBkdAZdb6rvLHMmgHTegDaAhHQJSS4vtdAxB1fZQoaAZHQG0DPmxMWXVoB01pAWgIR0CUk4X2dupCdWUu"
+    },
+    "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  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": 0.999,
+    "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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"
+    }
+}

Maly_ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbde057cf07105038fee11a7e482009e6479fa7f08adcd5dfd60b624ffaaf1a8
+size 88362

Maly_ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77f76b0c3c6ca1336a6dbb007ae6953e22b9e261b7e8c8ad66a846243e979438
+size 43762

Maly_ppo-LunarLander-v2/pytorch_variables.pth

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

Maly_ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: MlpPolicy
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 256.72 +/- 22.84
+      name: mean_reward
+      verified: false
+---
+
+# **MlpPolicy** Agent playing **LunarLander-v2**
+This is a trained model of a **MlpPolicy** 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 0x7ae21a5a2440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ae21a5a24d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ae21a5a2560>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ae21a5a25f0>", "_build": "<function ActorCriticPolicy._build at 0x7ae21a5a2680>", "forward": "<function ActorCriticPolicy.forward at 0x7ae21a5a2710>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ae21a5a27a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ae21a5a2830>", "_predict": "<function ActorCriticPolicy._predict at 0x7ae21a5a28c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ae21a5a2950>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ae21a5a29e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ae21a5a2a70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ae21a52e1c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1727034891746555220, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAADfB71Io4C6xDc2uQZHKrQUV2e5VshUOAAAgD8AAIA/tnGxPgywdz+6Vy8+Whe1vmQRZz4iCp68AAAAAAAAAABm7mM7u872PkaN5j2ucEq+Kpw1PYaSOrwAAAAAAAAAAO1Abj581Cg/GIpWvGqhrb7T5Qk+sToFvQAAAAAAAAAATbprPfb4MbruYNe6cdMRN1pVArvNUIO2AACAPwAAgD8z6/E8j/JcuuM747pcgwO2y4YMu8u8BToAAIA/AACAP5potDzsUdS5oI83OnfNFbRHugu7le1YuQAAgD8AAIA/mohDvcPxRror9Hi6LejXteD8izsgLZE5AACAPwAAgD+NV6k9ZCD2Pg+ior3VUZ6+CtcuPGOjl70AAAAAAAAAAACSnjzDJRm6at9NOuYxJDT5aie7CBByuQAAgD8AAIA/zTIOPbi21LmglJs4pJu0M1AGy7ohYbS3AACAPwAAgD8AdvI8XKs5utWi8brEB262y6iUO/baDDoAAIA/AACAP7Ovmj3DSRO69r+4OO1GpzM5xjO5mFzUtwAAgD8AAIA/5vlbPXtGhLpxV6O2seCkscq9ijp3VME1AACAPwAAgD8Aubu8pDA7uXTiMrqMT6Y1Uf+au53IWTkAAIA/AACAP2Zm5DoUgpS654eyuolnsrWFDBY72MvOOQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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  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": 0.999, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.7213484, "std_reward": 22.835831662293, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-09-22T20:17:03.111916"}