Lunar-Landing model 1st iteration

PPO-LunarLander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f51531f59df4e5909ef9bcfe31ce174cbe6e0b10a85c58e20485b8315fc5db83
+size 147324

PPO-LunarLander/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

PPO-LunarLander/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f75f2524b80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75f2524c10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f75f2524ca0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f75f2524d30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f75f2524dc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f75f2524e50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75f2524ee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f75f2524f70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f75f2526040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f75f25260d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f75f2526160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f75f25261f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f75f25a32a0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1675936331745080153,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 310,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

PPO-LunarLander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9bbf8a471649de2945d3773c1061dbc074f831ab44c802fb0739ee1849a3b6e
+size 87929

PPO-LunarLander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7c361322f112b66187e25a64388c265d7c4d90a6cfdabf588cb5f52e844f9af
+size 43393

PPO-LunarLander/pytorch_variables.pth

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

PPO-LunarLander/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

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: 265.76 +/- 44.28
+      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 0x7f75f2524b80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75f2524c10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f75f2524ca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f75f2524d30>", "_build": "<function ActorCriticPolicy._build at 0x7f75f2524dc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f75f2524e50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75f2524ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f75f2524f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f75f2526040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f75f25260d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f75f2526160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f75f25261f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f75f25a32a0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675936331745080153, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVNBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIp1zhXa4rcUCUhpRSlIwBbJRNJAKMAXSUR0ClCJRUedTYdX2UKGgGaAloD0MI5US7CikXWUCUhpRSlGgVTegDaBZHQKUImS7oSth1fZQoaAZoCWgPQwh+VS5U/pVyQJSGlFKUaBVNeAFoFkdApQjyD5CWvHV9lChoBmgJaA9DCAfQ7/s3iG9AlIaUUpRoFUvmaBZHQKUI9IJ7b+N1fZQoaAZoCWgPQwhxGw3gre9vQJSGlFKUaBVL1GgWR0ClCR5SvTw2dX2UKGgGaAloD0MIfUELCVhicUCUhpRSlGgVS8xoFkdApQkr8gpz93V9lChoBmgJaA9DCOW4UzpY3nBAlIaUUpRoFUvfaBZHQKUJSGahHsl1fZQoaAZoCWgPQwjGNqloLCNiQJSGlFKUaBVN6ANoFkdApQnie7L+xXV9lChoBmgJaA9DCHEDPj+MPm9AlIaUUpRoFUvRaBZHQKULDvQ4S6F1fZQoaAZoCWgPQwjulXmr7u1wQJSGlFKUaBVNawFoFkdApQvRkZrHl3V9lChoBmgJaA9DCGL4iJhSQHJAlIaUUpRoFUvdaBZHQKUMALncL0B1fZQoaAZoCWgPQwgTEJNwITByQJSGlFKUaBVLxmgWR0ClDAWtEG7jdX2UKGgGaAloD0MInxwFiIIOcUCUhpRSlGgVTaoBaBZHQKUMDjYqXnh1fZQoaAZoCWgPQwiLiGLyhspiQJSGlFKUaBVN6ANoFkdApQwj2tdRi3V9lChoBmgJaA9DCCv8Gd7sNXBAlIaUUpRoFUvHaBZHQKUMP8gIQe51fZQoaAZoCWgPQwimKQKcXnduQJSGlFKUaBVL8mgWR0ClDE+XRgJDdX2UKGgGaAloD0MIXAGFenqLckCUhpRSlGgVS+loFkdApQx7FwT/Q3V9lChoBmgJaA9DCGMK1jhbEHJAlIaUUpRoFU1NAWgWR0ClDI/ub7TEdX2UKGgGaAloD0MIPjxLkFFOc0CUhpRSlGgVS9hoFkdApQyS9RJmNHV9lChoBmgJaA9DCAVqMXiY1W5AlIaUUpRoFUvGaBZHQKUM3OpKjBV1fZQoaAZoCWgPQwjcZFQZhh9yQJSGlFKUaBVNUAFoFkdApQ1k74i5eHV9lChoBmgJaA9DCNPcCmF1gXBAlIaUUpRoFUvraBZHQKUOVpblijN1fZQoaAZoCWgPQwg6kst/yM9tQJSGlFKUaBVLxmgWR0ClDpmsvIwNdX2UKGgGaAloD0MIknh5OleWbUCUhpRSlGgVS8xoFkdApQ7WHpKSPnV9lChoBmgJaA9DCK7TSEslvXBAlIaUUpRoFUvAaBZHQKUO2rFOwgV1fZQoaAZoCWgPQwizQpHu5zpxQJSGlFKUaBVL4WgWR0ClDuKxC6YmdX2UKGgGaAloD0MIhA8lWjIYckCUhpRSlGgVS99oFkdApQ764pc5bXV9lChoBmgJaA9DCAVtcvgke3NAlIaUUpRoFUvOaBZHQKUPPKujh1l1fZQoaAZoCWgPQwjhs3Vw8DVwQJSGlFKUaBVLzmgWR0ClD1JjMFEBdX2UKGgGaAloD0MIeuBjsGKAcUCUhpRSlGgVS/RoFkdApQ+DY7JXAHV9lChoBmgJaA9DCAvPS8UGpXBAlIaUUpRoFU1AAWgWR0ClEHoo3JgcdX2UKGgGaAloD0MIvceZJuyPckCUhpRSlGgVTSQBaBZHQKURBvG6wt91fZQoaAZoCWgPQwhs7X2qysNxQJSGlFKUaBVL+2gWR0ClERvK+zt1dX2UKGgGaAloD0MIz4WRXlSGcECUhpRSlGgVS81oFkdApRG1eMQ2/HV9lChoBmgJaA9DCHjwEwcQuHFAlIaUUpRoFUvvaBZHQKUR/7Vrhzh1fZQoaAZoCWgPQwjHLebnhlByQJSGlFKUaBVNfAFoFkdApRIENWluWXV9lChoBmgJaA9DCE/MejHURHFAlIaUUpRoFUvLaBZHQKUSFFI/Z/V1fZQoaAZoCWgPQwht4Xmp2NhvQJSGlFKUaBVL42gWR0ClEktx+8XfdX2UKGgGaAloD0MIDhZO0vzibUCUhpRSlGgVS+9oFkdApRKDytmthnV9lChoBmgJaA9DCFKco44OtG9AlIaUUpRoFUvvaBZHQKUS5dVNpM91fZQoaAZoCWgPQwiDiNS0y2xxQJSGlFKUaBVL7mgWR0ClEvfU4JeFdX2UKGgGaAloD0MITRHg9G5HckCUhpRSlGgVS+xoFkdApRMneFcps3V9lChoBmgJaA9DCHaqfM/IInJAlIaUUpRoFU0dAWgWR0ClEy7M5fdAdX2UKGgGaAloD0MIZVWEm8wocUCUhpRSlGgVS79oFkdApRNkMVk+YHV9lChoBmgJaA9DCOc6jbTUunFAlIaUUpRoFUvQaBZHQKUUL2QGOdZ1fZQoaAZoCWgPQwiG5jqNNIBvQJSGlFKUaBVL4GgWR0ClFINfgJkYdX2UKGgGaAloD0MIhxkaTwQ9Y0CUhpRSlGgVTegDaBZHQKUUnZJ04ip1fZQoaAZoCWgPQwgyHqUSXjNwQJSGlFKUaBVLw2gWR0ClFPk2Hck/dX2UKGgGaAloD0MItcTKaGRxcECUhpRSlGgVS/FoFkdApRVQYNy5qnV9lChoBmgJaA9DCGA6rdsgGXFAlIaUUpRoFUvQaBZHQKUVWxVQyh11fZQoaAZoCWgPQwhTswdaAclxQJSGlFKUaBVL7mgWR0ClFYhdt2s8dX2UKGgGaAloD0MIFR3J5T8dZUCUhpRSlGgVTegDaBZHQKUV356+nIh1fZQoaAZoCWgPQwjJIk28wzFyQJSGlFKUaBVNEAFoFkdApRX78DSw4nV9lChoBmgJaA9DCE6Zm2/E4W9AlIaUUpRoFUvEaBZHQKUV+us90Rx1fZQoaAZoCWgPQwiP+1brxBtwQJSGlFKUaBVL0WgWR0ClFiy75Ec9dX2UKGgGaAloD0MI5PVgUrxxcUCUhpRSlGgVS+ZoFkdApRY2qPwNLHV9lChoBmgJaA9DCDZzSGohP29AlIaUUpRoFUvxaBZHQKUWYmCyyD91fZQoaAZoCWgPQwjkTX6LzrZwQJSGlFKUaBVNHwFoFkdApRaQIldC3XV9lChoBmgJaA9DCHnNqzrrknFAlIaUUpRoFUvWaBZHQKUXCpsoDxN1fZQoaAZoCWgPQwigpSvYRjdyQJSGlFKUaBVL0WgWR0ClFzvv0AcUdX2UKGgGaAloD0MIHk/LDxwWc0CUhpRSlGgVTR8BaBZHQKUXSAOrhit1fZQoaAZoCWgPQwj/kenQaQVxQJSGlFKUaBVL1mgWR0ClF2BV2icodX2UKGgGaAloD0MIhpDz/r8hYECUhpRSlGgVTegDaBZHQKUXchMajvd1fZQoaAZoCWgPQwgsnQ/PkphwQJSGlFKUaBVL0WgWR0ClF5h0ZFXrdX2UKGgGaAloD0MIsg5HV6licECUhpRSlGgVS8RoFkdApRe03++/QHV9lChoBmgJaA9DCL+7lSV62XBAlIaUUpRoFUvYaBZHQKUYEabWmP51fZQoaAZoCWgPQwhKQbeXdChyQJSGlFKUaBVLxmgWR0ClGD27voeQdX2UKGgGaAloD0MIp1t2iP9AcECUhpRSlGgVS/1oFkdApRhZQHiWFHV9lChoBmgJaA9DCHWtvU+Vk3FAlIaUUpRoFUvHaBZHQKUYaakyk9F1fZQoaAZoCWgPQwjA6V28H1tuQJSGlFKUaBVLwGgWR0ClGIheHBUJdX2UKGgGaAloD0MIqfdUTntycECUhpRSlGgVS+JoFkdApRi7VSXMQnV9lChoBmgJaA9DCMh8QKCz33JAlIaUUpRoFU0EAWgWR0ClGNH2IwdsdX2UKGgGaAloD0MIizcyj3z7cECUhpRSlGgVS/toFkdApRjP4TK1X3V9lChoBmgJaA9DCN7KEp3lmXFAlIaUUpRoFUvvaBZHQKUZK9rXUYt1fZQoaAZoCWgPQwjmdcQhm0duQJSGlFKUaBVLxWgWR0ClGYLBj4HpdX2UKGgGaAloD0MINsr6zcRmcECUhpRSlGgVS+VoFkdApRm431jAi3V9lChoBmgJaA9DCEZEMXnDDXFAlIaUUpRoFUv+aBZHQKUZyxRl6JJ1fZQoaAZoCWgPQwjXijbHuZVwQJSGlFKUaBVL42gWR0ClGcqWLP2PdX2UKGgGaAloD0MIIxXGFgITcECUhpRSlGgVS+5oFkdApRnJ4D9wWHV9lChoBmgJaA9DCMHFihpMsG5AlIaUUpRoFUvWaBZHQKUZ+gjhUBJ1fZQoaAZoCWgPQwiJesGnebVwQJSGlFKUaBVL6WgWR0ClGg1B2OhkdX2UKGgGaAloD0MIqTEh5tLzckCUhpRSlGgVS8RoFkdApRpM4iosI3V9lChoBmgJaA9DCME24smuCXFAlIaUUpRoFUvXaBZHQKUawpz90ih1fZQoaAZoCWgPQwhxqrUwCzRwQJSGlFKUaBVL4mgWR0ClGx2l/H5rdX2UKGgGaAloD0MIOPktOpnucECUhpRSlGgVS75oFkdApRuMejmCAnV9lChoBmgJaA9DCJVliGOd7XBAlIaUUpRoFUvraBZHQKUbrowmE5B1fZQoaAZoCWgPQwiwOnKkc/RxQJSGlFKUaBVNFAFoFkdApRxAksz2vnV9lChoBmgJaA9DCP/PYb680m1AlIaUUpRoFUu+aBZHQKUcfcry1/l1fZQoaAZoCWgPQwjByMuamLdxQJSGlFKUaBVLymgWR0ClHQ4TCcgAdX2UKGgGaAloD0MI2GFM+rtOcECUhpRSlGgVS+RoFkdApR0TSRbKR3V9lChoBmgJaA9DCHhEheqmxnBAlIaUUpRoFUvgaBZHQKUdHGb1AZ91fZQoaAZoCWgPQwhbsb/snktyQJSGlFKUaBVNbwFoFkdApR08/+sHSnV9lChoBmgJaA9DCD52Fyipam9AlIaUUpRoFUvcaBZHQKUdfxwQ1791fZQoaAZoCWgPQwjshQK2g7ZxQJSGlFKUaBVL+2gWR0ClHY9kJ8fFdX2UKGgGaAloD0MIA7LXu/9XckCUhpRSlGgVS+VoFkdApR4KURnOB3V9lChoBmgJaA9DCJcd4h+27W1AlIaUUpRoFUvkaBZHQKUfHp+tr9F1fZQoaAZoCWgPQwg7j4r/u4luQJSGlFKUaBVNDgFoFkdApR+Q2ycCo3V9lChoBmgJaA9DCL/xtWeWUXJAlIaUUpRoFUvjaBZHQKUfr2f02+B1fZQoaAZoCWgPQwjhQEgWMPVHQJSGlFKUaBVLmWgWR0ClH/ngYP5IdX2UKGgGaAloD0MI6J/gYsWZckCUhpRSlGgVS95oFkdApSCjvE0iyXVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 265.76383637780265, "std_reward": 44.27866929774069, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-09T10:20:25.191081"}