Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaPickAndPlace-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaPickAndPlace-v3
+      type: PandaPickAndPlace-v3
+    metrics:
+    - type: mean_reward
+      value: -50.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaPickAndPlace-v3**
+This is a trained model of a **A2C** agent playing **PandaPickAndPlace-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-PandaPickAndPlace-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33799f0d081544607e47457ae53ea6002b0f3227f92b682d2bd5ebda0e488e0b
+size 127419

a2c-PandaPickAndPlace-v3/_stable_baselines3_version

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

a2c-PandaPickAndPlace-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 0x7e7ca0bb1630>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e7ca0b9f000>"
+    },
+    "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": 1742483785519657338,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-0.7354406   0.7180626   0.15367718]\n [-0.89146125  0.21702372  0.15366785]\n [ 0.98069984  0.8457967   0.1536826 ]\n [-1.0149783   0.2791033   0.15368369]]",
+        "desired_goal": "[[ 1.7024548  -0.85597306 -1.0881793 ]\n [ 0.03353113  0.97147334  1.0326658 ]\n [ 1.6650771   0.35177442 -0.12647511]\n [ 1.2264777   0.94381624  0.72849035]]",
+        "observation": "[[ 0.7117115  -0.77834255 -0.69021595 -0.24507344  0.06405657 -1.2969686\n  -0.94924885 -0.7354406   0.7180626   0.15367718 -0.03065959 -0.00947577\n  -0.01279181  0.03587886  0.0738448   0.0693535  -0.02674502 -0.02492763\n   0.01654904]\n [ 0.6573048  -0.51761454 -0.6305533  -0.62730896 -0.12881994 -0.8661978\n   1.1234497  -0.89146125  0.21702372  0.15366785 -0.03053194 -0.00952935\n  -0.01380718  0.03537073  0.07409553  0.0693535  -0.02674504 -0.02492761\n   0.01604865]\n [ 0.40869626 -0.37061915 -0.28117192 -0.11437543 -1.3925258  -1.0449212\n  -0.9190696   0.98069984  0.8457967   0.1536826  -0.03080886 -0.00963068\n  -0.0134423   0.03538499  0.07435851  0.06950618 -0.02859284 -0.02618444\n   0.01625407]\n [ 0.63761705 -0.78547263 -0.58437926  0.97693306  1.6973275   0.41786367\n  -0.9488013  -1.0149783   0.2791033   0.15368369 -0.03075227 -0.00950759\n  -0.01314159  0.03551139  0.07409083  0.0693535  -0.02674504 -0.02492761\n   0.01617436]]"
+    },
+    "_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": "[[ 0.04565367  0.02106008  0.02      ]\n [ 0.07010525  0.10638062  0.02      ]\n [-0.13789472 -0.06778959  0.02      ]\n [ 0.05950556  0.08263981  0.02      ]]",
+        "desired_goal": "[[-0.05992157  0.03744504  0.02      ]\n [ 0.11904471  0.11369735  0.05117115]\n [ 0.12976782 -0.13288824  0.02      ]\n [-0.00551336 -0.04011539  0.11897891]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00  0.0000000e+00  4.5653667e-02\n   2.1060077e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00  7.0105247e-02\n   1.0638062e-01  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00 -1.3789472e-01\n  -6.7789592e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00  5.9505556e-02\n   8.2639813e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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, (19,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_shape": [
+            4
+        ],
+        "low": "[-1. -1. -1. -1.]",
+        "high": "[1. 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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"
+    }
+}

a2c-PandaPickAndPlace-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88a6dfbb6b7d21d2ffad57bd7fad3e6e4c8fc6ccc486a6395168fba430c6756c
+size 55368

a2c-PandaPickAndPlace-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c650ff93dd50d5eaa19281175de3ff0f8a08f7f10ef920f0adf2d292b932a1e2
+size 53359

a2c-PandaPickAndPlace-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

a2c-PandaPickAndPlace-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.6.56+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Nov 10 10:07:59 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.5.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.29.0
+- 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 0x7e7ca0bb1630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e7ca0b9f000>"}, "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": 1742483785519657338, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.7354406   0.7180626   0.15367718]\n [-0.89146125  0.21702372  0.15366785]\n [ 0.98069984  0.8457967   0.1536826 ]\n [-1.0149783   0.2791033   0.15368369]]", "desired_goal": "[[ 1.7024548  -0.85597306 -1.0881793 ]\n [ 0.03353113  0.97147334  1.0326658 ]\n [ 1.6650771   0.35177442 -0.12647511]\n [ 1.2264777   0.94381624  0.72849035]]", "observation": "[[ 0.7117115  -0.77834255 -0.69021595 -0.24507344  0.06405657 -1.2969686\n  -0.94924885 -0.7354406   0.7180626   0.15367718 -0.03065959 -0.00947577\n  -0.01279181  0.03587886  0.0738448   0.0693535  -0.02674502 -0.02492763\n   0.01654904]\n [ 0.6573048  -0.51761454 -0.6305533  -0.62730896 -0.12881994 -0.8661978\n   1.1234497  -0.89146125  0.21702372  0.15366785 -0.03053194 -0.00952935\n  -0.01380718  0.03537073  0.07409553  0.0693535  -0.02674504 -0.02492761\n   0.01604865]\n [ 0.40869626 -0.37061915 -0.28117192 -0.11437543 -1.3925258  -1.0449212\n  -0.9190696   0.98069984  0.8457967   0.1536826  -0.03080886 -0.00963068\n  -0.0134423   0.03538499  0.07435851  0.06950618 -0.02859284 -0.02618444\n   0.01625407]\n [ 0.63761705 -0.78547263 -0.58437926  0.97693306  1.6973275   0.41786367\n  -0.9488013  -1.0149783   0.2791033   0.15368369 -0.03075227 -0.00950759\n  -0.01314159  0.03551139  0.07409083  0.0693535  -0.02674504 -0.02492761\n   0.01617436]]"}, "_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": "[[ 0.04565367  0.02106008  0.02      ]\n [ 0.07010525  0.10638062  0.02      ]\n [-0.13789472 -0.06778959  0.02      ]\n [ 0.05950556  0.08263981  0.02      ]]", "desired_goal": "[[-0.05992157  0.03744504  0.02      ]\n [ 0.11904471  0.11369735  0.05117115]\n [ 0.12976782 -0.13288824  0.02      ]\n [-0.00551336 -0.04011539  0.11897891]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00  0.0000000e+00  4.5653667e-02\n   2.1060077e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00  7.0105247e-02\n   1.0638062e-01  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00 -1.3789472e-01\n  -6.7789592e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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  0.0000000e+00  5.9505556e-02\n   8.2639813e-02  2.0000000e-02  0.0000000e+00 -0.0000000e+00\n   0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n   0.0000000e+00  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, (19,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True]", "bounded_above": "[ True  True  True  True]", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 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-6.6.56+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Nov 10 10:07:59 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.29.0", "OpenAI Gym": "0.25.2"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:884da92de1d326ffd3e395f6c05457fe0734813c6252f7a68ce8f878acd526e1
+size 680851

results.json

@@ -0,0 +1 @@
+{"mean_reward": -50.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-03-20T16:02:24.527100"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b14072f357b95ac913af78d40487b2623af6783e3bc27bff4479007210c41340
+size 3013