Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1413.42 +/- 151.86
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+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
+
+...
+```

a2c-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84ab9539b0d3af7e34989000a87bfb1b08d08119c594a3a8f1981df9fd8d9d17
+size 129246

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7de05f7f2290>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7de05f7f2320>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7de05f7f23b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7de05f7f2440>",
+        "_build": "<function ActorCriticPolicy._build at 0x7de05f7f24d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7de05f7f2560>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7de05f7f25f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7de05f7f2680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7de05f7f2710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7de05f7f27a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7de05f7f2830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7de05f7f28c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7de05f7e9e80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690201301859290852,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_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": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f77684dc265612c4d1171749f985e5dae7845a03da555973e1a8b3017459b65
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c1d776e93de9d4e572e79a97c6940a14bce16bca6dcbbc6f2dfe5e2cf959059
+size 56894

a2c-AntBulletEnv-v0/pytorch_variables.pth

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

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

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 0x7de05f7f2290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7de05f7f2320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7de05f7f23b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7de05f7f2440>", "_build": "<function ActorCriticPolicy._build at 0x7de05f7f24d0>", "forward": "<function ActorCriticPolicy.forward at 0x7de05f7f2560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7de05f7f25f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7de05f7f2680>", "_predict": "<function ActorCriticPolicy._predict at 0x7de05f7f2710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7de05f7f27a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7de05f7f2830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7de05f7f28c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7de05f7e9e80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690201301859290852, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAKo75b8OtMw+XzTwPnzRk78QQKe9VUeIPUmW7z3kKMw++72kPf+pJDxq60S/E3RmvPBzGL/V6YU81kMzPyJfGD1YtKc/+obAuyckVj8og948s3Qhv7q8MzxPsFe/woIuvNq3Tz8hdAE/bPkGPzedUz8+7pi/qHmLPxpH3bx1tT0+TqrwPk1pDsDCQQY+a0YqPgmA+76k184+G76xvnboPT880Vq/j5aZv9s3Mz/80ZW+ELShP3xVFr87kRg/H9CdPphvI7/3fdM9nXMYv8eLHr/at08/GSD9v6rF8r8P2Zq/vgl+P2PuOz94h5M+N8NlPxtIp73SfTg/7GBov0uXsb93d0g/4TWfvKgvRD8zJ9s+DN2Bv6BTWz+V//I+ROeBPidIVr4ibA8/Ku1XvmUM4777USG/k16ePGjYDb4l7/s/2rdPPyF0AT9s+QY/D9mav4/A3z3GCwE/k9rYPq86XT/1SrU/sX6Iv+BL476C8bG+Yxvyvfy3Eb+taKu+ThDJP4moQr+vOsG+3P0mPwUNqr5rnBg/cp3dvhYkgb5+56U/Go0mv3XlUT8IRQ0/AKF6v5HAnb8ZIP2/bPkGPw/Zmr+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJ3V97/n4fyMAWyUTegDjAF0lEdAqa4P69CeE3V9lChoBkdAnv/UbcXWOWgHTegDaAhHQKmyGwQDmr91fZQoaAZHQJ0mGr4nF5xoB03oA2gIR0CpsrG8mKIjdX2UKGgGR0Cd6B25QP7OaAdN6ANoCEdAqbPm/i5uqHV9lChoBkdAnUeKLn9vTGgHTegDaAhHQKm6LhYvFm51fZQoaAZHQJrClCSidrhoB03oA2gIR0Cpv/1FQVKxdX2UKGgGR0Cckx0DU3GXaAdN6ANoCEdAqcCTDsMRYnV9lChoBkdAnKsubRWtEGgHTegDaAhHQKnB0RywOe91fZQoaAZHQJuO/9gnc+JoB03oA2gIR0CpyAVdHDrJdX2UKGgGR0CeQlWTHKfWaAdN6ANoCEdAqcwh5HEuQXV9lChoBkdAnYWp9ZzPr2gHTegDaAhHQKnMtuVopQV1fZQoaAZHQJ0IcGs3hn9oB03oA2gIR0CpzfS57PY4dX2UKGgGR0Cb5t3eN1hcaAdN6ANoCEdAqdR+1MM7VHV9lChoBkdAm6gzpkf9xmgHTegDaAhHQKnaB/82rGR1fZQoaAZHQJwvrdBSk0toB03oA2gIR0Cp2p7a7EpBdX2UKGgGR0Cd/pwCKaXsaAdN6ANoCEdAqdvhKnNxEXV9lChoBkdAlSV4EbHZK2gHTegDaAhHQKniIVWS2Yx1fZQoaAZHQJo/wduHerNoB03oA2gIR0Cp5iihWYF8dX2UKGgGR0CM7QMzdk8SaAdN6ANoCEdAqea/tD2JznV9lChoBkdAk/aY2jwhGGgHTegDaAhHQKnn+cx0uDl1fZQoaAZHQJqj3FfiPyVoB03oA2gIR0Cp7sgNoakzdX2UKGgGR0Cb1waVD8cdaAdN6ANoCEdAqfQJ95QgtHV9lChoBkdAngRBzNliB2gHTegDaAhHQKn0p4Ju2ql1fZQoaAZHQJ9LPmaH9FZoB03oA2gIR0Cp9eW/JvHcdX2UKGgGR0CJnqt7rs0IaAdN6ANoCEdAqfwPyI55q3V9lChoBkdAm/8GM85jpmgHTegDaAhHQKoALGm1pkB1fZQoaAZHQJvFqpxWDHxoB03oA2gIR0CqAMq4pc5bdX2UKGgGR0Ce0BIkZ75VaAdN6ANoCEdAqgH901ZTynV9lChoBkdAmTaBY3eenWgHTegDaAhHQKoJQqSX+l11fZQoaAZHQJuzuGsV+JBoB03oA2gIR0CqDhH9WIXTdX2UKGgGR0CUfKM98qnWaAdN6ANoCEdAqg6qE6DGtXV9lChoBkdAmzOJI6KceGgHTegDaAhHQKoP8OEug6F1fZQoaAZHQHlPcTSLIghoB03oA2gIR0CqFngbyYoidX2UKGgGR0CZs9Lx7RfGaAdN6ANoCEdAqhp6InBtUHV9lChoBkdAmRQiZ0CA+mgHTegDaAhHQKobDeJHiFV1fZQoaAZHQJsTrdk8RthoB03oA2gIR0CqHEecx0uEdX2UKGgGR0Cb3aLs8gZCaAdN6ANoCEdAqiQ4LE1l5HV9lChoBkdAjIniK77KrGgHTegDaAhHQKoobwkxASp1fZQoaAZHQJysB9ph4MZoB03oA2gIR0CqKQdFvybydX2UKGgGR0CcVQGOdXkpaAdN6ANoCEdAqipFFjNILHV9lChoBkdAmOKIj4YaYWgHTegDaAhHQKownX8O09h1fZQoaAZHQI+GHVRUFStoB03oA2gIR0CqNMRgiNbUdX2UKGgGR0CLnpAN5MURaAdN6ANoCEdAqjVgarFOwnV9lChoBkdAkXe8QumJnGgHTegDaAhHQKo2oGwiaAp1fZQoaAZHQIlnRoTPBzpoB03oA2gIR0CqPs/ReC04dX2UKGgGR0CRsmBxxT86aAdN6ANoCEdAqkL608eS0XV9lChoBkdAlRuGsNlRQGgHTegDaAhHQKpDkuKXOW11fZQoaAZHQJPDrjrAxi5oB03oA2gIR0CqRMhD5TIedX2UKGgGR0CUePNkvsZ6aAdN6ANoCEdAqktD7sOXmnV9lChoBkdAnl6X9itq6GgHTegDaAhHQKpPYlgMMJB1fZQoaAZHQJp9gNnXd0toB03oA2gIR0CqT/xGlQ/HdX2UKGgGR0B/yNGBnSOSaAdN6ANoCEdAqlE7VhCtzXV9lChoBkdAe22upjtojGgHTegDaAhHQKpaqq6vq1R1fZQoaAZHQJ7RlyZKFqVoB03oA2gIR0CqXxj28IzFdX2UKGgGR0CeGIN0/4ZdaAdN6ANoCEdAql+wEQoTf3V9lChoBkdAnprK2SdOI2gHTegDaAhHQKpg3zTWoWJ1fZQoaAZHQJthApVjqfRoB03oA2gIR0CqZw/ub7TEdX2UKGgGR0Cef8NedCmeaAdN6ANoCEdAqmsUk0JnhHV9lChoBkdAf2QsuWa+e2gHTegDaAhHQKprsGNaQmx1fZQoaAZHQJtSe4iHIp9oB03oA2gIR0CqbYTWf9P2dX2UKGgGR0CbDtIP9UCJaAdN6ANoCEdAqnToT0xubnV9lChoBkdAmfgYsI3R5WgHTegDaAhHQKp4+WEbo8p1fZQoaAZHQJ2lNsuWa+hoB03oA2gIR0CqeYx4ptrLdX2UKGgGR0Cc8kdrftQbaAdN6ANoCEdAqnrEona37XV9lChoBkdAk5pKBy0a62gHTegDaAhHQKqBAyPdVNp1fZQoaAZHQJd/GKCQLeBoB03oA2gIR0CqhTjoZAIIdX2UKGgGR0CCfpp6hQFcaAdN6ANoCEdAqoYd1QqI8HV9lChoBkdAmYsX6qKgqWgHTegDaAhHQKqH9qrR0EJ1fZQoaAZHQHwTzBEa2ndoB03oA2gIR0CqjvXFLnLadX2UKGgGR0CV1VKDCgscaAdN6ANoCEdAqpL+9+PRzHV9lChoBkdAmXIQZXMhYGgHTegDaAhHQKqTmpPykKx1fZQoaAZHQJj4+lhw2l5oB03oA2gIR0CqlM7Uoa1kdX2UKGgGR0CIlCJeE7GOaAdN6ANoCEdAqpsLfDUExXV9lChoBkdAmllytihFmWgHTegDaAhHQKqfeRV6u4h1fZQoaAZHQHXI6+JxecBoB03oA2gIR0CqoFVrIo3KdX2UKGgGR0CajPB7eEZjaAdN6ANoCEdAqqIvmPo3aXV9lChoBkdAlX+Kx1PnCGgHTegDaAhHQKqo7eSjgyd1fZQoaAZHQIfK4ouwostoB03oA2gIR0CqrR3g9/z8dX2UKGgGR0CUjOg+hXbNaAdN6ANoCEdAqq2ysny/bnV9lChoBkdAmfPoB/7SA2gHTegDaAhHQKqu6g2ZRbd1fZQoaAZHQICweRoysS1oB03oA2gIR0CqtZ7pNbkfdX2UKGgGR0CN1O7nxJ/YaAdN6ANoCEdAqrrtx2jfvXV9lChoBkdAhMLxmTTvzGgHTegDaAhHQKq75IFNcnp1fZQoaAZHQJJla6iCaqloB03oA2gIR0CqvXFar3j/dX2UKGgGR0CB4XFgDzRQaAdN6ANoCEdAqsPcnb7CSHV9lChoBkdAkBf2tp22X2gHTegDaAhHQKrH/QQ+UyJ1fZQoaAZHQI7vNZFG5MFoB03oA2gIR0CqyJIGhVU/dX2UKGgGR0CWSDKUmlZYaAdN6ANoCEdAqsnGC7K7qnV9lChoBkdAl+7dqtYCAGgHTegDaAhHQKrP8cdYGMZ1fZQoaAZHQJm9HblA/s5oB03oA2gIR0Cq1Z2PDHfedX2UKGgGR0CVKfuHN5dGaAdN6ANoCEdAqtZM41gpjXV9lChoBkdAkzWZkTYdyWgHTegDaAhHQKrXhw5vLox1fZQoaAZHQJdlNrZamoBoB03oA2gIR0Cq3cAYP5HmdX2UKGgGR0CcuGHaN+9baAdN6ANoCEdAquHKaoddV3V9lChoBkdAl2475mAbymgHTegDaAhHQKriZKB/Zuh1fZQoaAZHQJpFKL5ylvZoB03oA2gIR0Cq45Xt0FKTdX2UKGgGR0CYoG2c8TzvaAdN6ANoCEdAqunx0KZ2IXV9lChoBkdAlcSzZlFtsWgHTegDaAhHQKrvxE61b7l1fZQoaAZHQJeMhbeMyadoB03oA2gIR0Cq8Fo11nuidX2UKGgGR0CVtGGbTc7AaAdN6ANoCEdAqvGKeXiR4nVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1413.4163338001702, "std_reward": 151.86336493315363, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-24T13:27:49.939215"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85a49dd860706e5a0fa9cdaf8d6674b3317695f29f8c20e0921a41c62db1ee65
+size 2169