Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.19 +/- 0.10
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff65468c4a359628f14d78d096b51607c7f1ea5ff7a0920513daa6ff20a0bdcb
+size 106831

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "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 0x785a25c05750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x785a25c034c0>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1693909377764387454,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-0.61057913 -0.4951661   0.33884034]\n [-0.53295904 -0.49309325  0.37255698]\n [-0.17871705  0.47425726 -0.11495545]\n [-0.41251394  0.98919564 -1.3351692 ]]",
+        "desired_goal": "[[-0.37125215 -0.78102756  0.46553534]\n [-0.44073644 -0.6243849   1.5130959 ]\n [-0.97242457  1.407406   -0.05710375]\n [-0.12989713  0.52402264 -1.3993962 ]]",
+        "observation": "[[-0.61057913 -0.4951661   0.33884034 -0.81158745 -1.633959    0.89221185]\n [-0.53295904 -0.49309325  0.37255698 -0.45403054 -1.6399153   0.98080593]\n [-0.17871705  0.47425726 -0.11495545 -1.1648097   1.6727035  -0.91342103]\n [-0.41251394  0.98919564 -1.3351692  -0.258829   -0.01292646 -1.117932  ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.0400442  -0.0632148   0.16928875]\n [ 0.09668508  0.02921349  0.02912518]\n [-0.06070114 -0.0951366   0.24358772]\n [-0.01862331 -0.14714374  0.03252233]]",
+        "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,
+    "_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": 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,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuDQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8470f2fe63a77a965f2e64bfbe8e008e9f03ad1776fc3fe3debfdb59fd7754bd
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0436534bf1bd3db8361af313a702aac3354731c67f76eeca2251ce0c89daf0d
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

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

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.25.2

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 0x785a25c05750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x785a25c034c0>"}, "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}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693909377764387454, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.61057913 -0.4951661   0.33884034]\n [-0.53295904 -0.49309325  0.37255698]\n [-0.17871705  0.47425726 -0.11495545]\n [-0.41251394  0.98919564 -1.3351692 ]]", "desired_goal": "[[-0.37125215 -0.78102756  0.46553534]\n [-0.44073644 -0.6243849   1.5130959 ]\n [-0.97242457  1.407406   -0.05710375]\n [-0.12989713  0.52402264 -1.3993962 ]]", "observation": "[[-0.61057913 -0.4951661   0.33884034 -0.81158745 -1.633959    0.89221185]\n [-0.53295904 -0.49309325  0.37255698 -0.45403054 -1.6399153   0.98080593]\n [-0.17871705  0.47425726 -0.11495545 -1.1648097   1.6727035  -0.91342103]\n [-0.41251394  0.98919564 -1.3351692  -0.258829   -0.01292646 -1.117932  ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.0400442  -0.0632148   0.16928875]\n [ 0.09668508  0.02921349  0.02912518]\n [-0.06070114 -0.0951366   0.24358772]\n [-0.01862331 -0.14714374  0.03252233]]", "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, "_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": 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, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.185803328640759, "std_reward": 0.10107925246484992, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-05T11:12:24.365181"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e173e73308e18e2e17df31ba49c34bf51e33ee7c502b0864e89d0baca8870cc0
+size 2623