Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -2.39 +/- 0.97
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-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
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c497dd2c9ef084a4d21b065e632ddc903d7a30dc7c3d8400938d9157734e7be1
+size 108131

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f4e35dbae50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4e35db2e10>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1675850022381504046,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]]",
+        "desired_goal": "[[-0.15774313  1.5142889  -0.01002938]\n [-0.6312523   1.1325519  -1.692352  ]\n [ 1.1423361   1.3136591   1.4431556 ]\n [ 1.1028011  -0.91591996 -0.68633986]]",
+        "observation": "[[ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.14907321 -0.00615256  0.15857649]\n [ 0.14659837 -0.05230851  0.11882916]\n [ 0.14091448  0.00034326  0.10486803]\n [-0.04484547  0.006438    0.04764004]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33ff6919ddd04cf82b7ddb87e80f2b26662c806b13c3c49d33a461d61f36f6ab
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b442e748b1622d9b90a269a31d44be121f4cb87ca7f1e146dec9ea200ba8d76a
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

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

a2c-PandaReachDense-v2/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

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f4e35dbae50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4e35db2e10>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675850022381504046, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]\n [4.2076439e-01 3.4935848e-04 5.7457870e-01]]", "desired_goal": "[[-0.15774313  1.5142889  -0.01002938]\n [-0.6312523   1.1325519  -1.692352  ]\n [ 1.1423361   1.3136591   1.4431556 ]\n [ 1.1028011  -0.91591996 -0.68633986]]", "observation": "[[ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]\n [ 4.2076439e-01  3.4935848e-04  5.7457870e-01  9.1499044e-03\n  -1.3646170e-03  9.1009522e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.14907321 -0.00615256  0.15857649]\n [ 0.14659837 -0.05230851  0.11882916]\n [ 0.14091448  0.00034326  0.10486803]\n [-0.04484547  0.006438    0.04764004]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "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": -2.3886768926633524, "std_reward": 0.9736013084626763, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-08T11:21:54.294591"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dae426cda73e040865e6f9dcc04c178034fc03dbb7823e6835e30b337fc11e2
+size 3056