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: -1.04 +/- 0.13
+      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 sha256:4aba4a9f55a6a0c1fce8db0c6c67bfaadf05bebf40bd628ef1561c85bbc304b4
+size 108015

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 0x7f0a24b0ef70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f0a24a8fe00>"
+    },
+    "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": 1679928034814410139,
+    "learning_rate": 0.0001,
+    "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": "[[0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]]",
+        "desired_goal": "[[ 1.4771963   1.7042978  -0.5019912 ]\n [-1.1899054  -1.1681492  -1.5024163 ]\n [ 0.4468176  -0.9726357   0.04854291]\n [ 0.41201866  0.13906798  1.2160425 ]]",
+        "observation": "[[ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]]"
+    },
+    "_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": "[[-0.08183409  0.11049373  0.19529171]\n [ 0.01166716 -0.08293857  0.11369769]\n [ 0.03624942 -0.09774753  0.06242481]\n [ 0.1360969   0.08133775  0.09347287]]",
+        "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": true
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22d451a93cc73addb79218ce0d864793d06e64b5da0114b6789f5d47210cbad9
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3218109c12c30e1db7a9c3ad68b505dded85fff9f55dd40e79c1338237412b6
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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 0x7f0a24b0ef70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f0a24a8fe00>"}, "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:": "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", "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": 1679928034814410139, "learning_rate": 0.0001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/Gjbi6xxDLYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]\n [0.27097714 0.00621069 0.5395684 ]]", "desired_goal": "[[ 1.4771963   1.7042978  -0.5019912 ]\n [-1.1899054  -1.1681492  -1.5024163 ]\n [ 0.4468176  -0.9726357   0.04854291]\n [ 0.41201866  0.13906798  1.2160425 ]]", "observation": "[[ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]\n [ 0.27097714  0.00621069  0.5395684   0.00183812 -0.00167927  0.01772441]]"}, "_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": "[[-0.08183409  0.11049373  0.19529171]\n [ 0.01166716 -0.08293857  0.11369769]\n [ 0.03624942 -0.09774753  0.06242481]\n [ 0.1360969   0.08133775  0.09347287]]", "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": true, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.0429694641148672, "std_reward": 0.12857346168818692, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T15:28:32.398118"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0f882087e866f4e269a102898342eb2b1ec0f8f11d6e6f6ca2e3e39f1780cbd
+size 3056