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: -0.18 +/- 0.07
+      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:af4f24edfb116e93cc2129002a0360dfd48a61e4dfab60b5e5639f88b2b350f3
+size 108251

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 0x7fe5feeb39a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe5feeae880>"
+    },
+    "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": 1697969462879586980,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-1.0531687e-01  4.6885365e-01 -2.3671322e-01]\n [ 2.5381237e-01  1.4413836e-03  4.2093030e-01]\n [-1.0586361e+00 -1.4774603e+00 -1.0739057e+00]\n [-2.1535511e-01 -1.3969159e+00 -8.9950317e-01]]",
+        "desired_goal": "[[ 0.31808302  0.2753831  -0.3065315 ]\n [-0.26970285  0.5786891   0.5307501 ]\n [-1.4830648  -0.9312354  -0.67175347]\n [ 0.13310996 -1.3953325  -0.25097534]]",
+        "observation": "[[-1.0531687e-01  4.6885365e-01 -2.3671322e-01 -1.8820937e+00\n   1.6185204e+00 -1.4027666e+00]\n [ 2.5381237e-01  1.4413836e-03  4.2093030e-01  4.8759189e-01\n  -4.2188829e-03  3.9118540e-01]\n [-1.0586361e+00 -1.4774603e+00 -1.0739057e+00 -7.6614118e-01\n  -9.7447717e-01 -2.3193631e-01]\n [-2.1535511e-01 -1.3969159e+00 -8.9950317e-01  1.4095596e+00\n  -8.5940725e-01 -9.3041465e-04]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.04809951  0.00277038  0.09255505]\n [ 0.08217524 -0.05426951  0.17455606]\n [ 0.10821397 -0.01851234  0.24253617]\n [-0.01285366 -0.14699642  0.09454554]]",
+        "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv9R8VpKzzEuMAWyUSwOMAXSUR0CkVVh0hePadX2UKGgGR7/Cnuy/sVtXaAdLAmgIR0CkVggGB4D+dX2UKGgGR7++57PY4ACGaAdLAmgIR0CkVbNTLns+dX2UKGgGR7/QrsjVx0dSaAdLBGgIR0CkVPpQcghbdX2UKGgGR7+8E0SAYpDvaAdLAmgIR0CkVhBInSfEdX2UKGgGR7+5QoCuEEkjaAdLAmgIR0CkVQI9cKPXdX2UKGgGR7/RiLVFx4puaAdLA2gIR0CkVb+nZTQ3dX2UKGgGR7/P5N47ihnKaAdLBGgIR0CkVWvsqrimdX2UKGgGR7/R9ytFKCg9aAdLA2gIR0CkVh9nbqQjdX2UKGgGR7/RjN6gM+eOaAdLA2gIR0CkVRGX5WRzdX2UKGgGR7/MJZ4fOlfraAdLBGgIR0CkVdLsjVx0dX2UKGgGR7/YGT9sJpnIaAdLBGgIR0CkVX8w5/9YdX2UKGgGR7/LGS6lLvkSaAdLA2gIR0CkViwnpjc3dX2UKGgGR7/PGwzLwF1TaAdLA2gIR0CkVR4/Vy3kdX2UKGgGR7/Fyhi9Zid8aAdLAmgIR0CkVjaqS5iFdX2UKGgGR7/GIVM23rleaAdLA2gIR0CkVeH1FpfydX2UKGgGR7/MJ7b+Lm6oaAdLA2gIR0CkVY47q6e5dX2UKGgGR7+nQnhKlHjIaAdLAWgIR0CkVZK3/givdX2UKGgGR7/IzyBkI5YHaAdLA2gIR0CkVS2dVea8dX2UKGgGR7/IBwMpgCwKaAdLA2gIR0CkVkPGIbfhdX2UKGgGR7/IiY9gWrOraAdLA2gIR0CkVe8kMTewdX2UKGgGR7/DhLGrCFbnaAdLAmgIR0CkVTX8wYcedX2UKGgGR7/JOGj9GZuyaAdLA2gIR0CkVZ+C04R3dX2UKGgGR7/EB+4LCvX9aAdLAmgIR0CkVk8D8tPIdX2UKGgGR7++IMz/IbOvaAdLAmgIR0CkVUFf7aZhdX2UKGgGR7/QKNyYG+sYaAdLA2gIR0CkVf/Ho5ggdX2UKGgGR7/GYdhiLEUCaAdLA2gIR0CkVbAR02cbdX2UKGgGR7/M3Ns3yZrpaAdLA2gIR0CkVlzundftdX2UKGgGR7/MjgydnTRZaAdLA2gIR0CkVU7uc+aCdX2UKGgGR7/UIYm9g4OuaAdLA2gIR0CkVg0NjLB9dX2UKGgGR7/J13dKujh2aAdLA2gIR0CkVcCbMHKPdX2UKGgGR7/V5YHPeHi4aAdLA2gIR0CkVm3Q2MsIdX2UKGgGR7/EmixmkFfRaAdLA2gIR0CkVV/EwWWQdX2UKGgGR7/VAEdNnGsFaAdLA2gIR0CkVh1hsqJ/dX2UKGgGR7/PkELYwqRVaAdLA2gIR0CkVnpsoDxLdX2UKGgGR7/XZ9NN8E3baAdLBGgIR0CkVdHZ9NN8dX2UKGgGR7/H3W4EwFkhaAdLA2gIR0CkVWyyD7IldX2UKGgGR7+SCrcTJyQxaAdLAWgIR0CkVoGm+CbudX2UKGgGR7+kbkwN9YwJaAdLAWgIR0CkVoXEZR8/dX2UKGgGR7/bLG7z06HTaAdLBGgIR0CkVjE+PikwdX2UKGgGR7/PolD4QBgeaAdLA2gIR0CkVXwrDqGDdX2UKGgGR7+3i0fHPu5SaAdLAmgIR0CkVo5sCT2WdX2UKGgGR7/aUrkKeCkHaAdLBGgIR0CkVeXv6TGHdX2UKGgGR7/SIrvsqrimaAdLA2gIR0CkVj5e7cwhdX2UKGgGR7+zqC6H0se5aAdLAmgIR0CkVYV/2Cd0dX2UKGgGR7/L+VC5VfeDaAdLA2gIR0CkVp7EYO2BdX2UKGgGR7/AB6KLsKLLaAdLAmgIR0CkVko0ZWJadX2UKGgGR7/KByS3b212aAdLA2gIR0CkVfZ9d/rjdX2UKGgGR7/PMX7+DOC5aAdLA2gIR0CkVZVoxpL3dX2UKGgGR7+1plBhQWN4aAdLAmgIR0CkVf77TDwZdX2UKGgGR7/IzabnX/YKaAdLA2gIR0CkVqvznRsudX2UKGgGR7/QA/cFhXr/aAdLA2gIR0CkVldKNAC5dX2UKGgGR7/BBbfP5YYBaAdLAmgIR0CkVgpAt4A0dX2UKGgGR7/Rb0e2d/ayaAdLA2gIR0CkVaUcOskqdX2UKGgGR7/N9VFQVKwqaAdLA2gIR0CkVrtorWiDdX2UKGgGR7/RIYFaB7NTaAdLA2gIR0CkVma9CeEqdX2UKGgGR7+wdxQzk6tDaAdLAmgIR0CkVa3R5TqCdX2UKGgGR7/F+AmReTmoaAdLA2gIR0CkVheHSF4+dX2UKGgGR7+xzbN8ma6SaAdLAmgIR0CkVm+iSJTEdX2UKGgGR7/MAKfFrEcbaAdLA2gIR0CkVsuYhMakdX2UKGgGR7+QqZtvXK8taAdLAWgIR0CkVnbeMyaedX2UKGgGR7/TxBmf5DZ2aAdLA2gIR0CkVb4GMXJpdX2UKGgGR7/S3qzJIUaiaAdLA2gIR0CkViemelKsdX2UKGgGR7++OwPiDM/yaAdLAmgIR0CkVcZvDP4VdX2UKGgGR7/Te2uxKQJYaAdLA2gIR0CkVoQVsUItdX2UKGgGR7+0OSW7e2uxaAdLAmgIR0CkVjBdD6WPdX2UKGgGR7/S1HvttyggaAdLA2gIR0CkVdXrt3OfdX2UKGgGR7/REgGKQ7tBaAdLA2gIR0CkVpNzjm0WdX2UKGgGR7/WaESM98qnaAdLA2gIR0CkVj+yiVSodX2UKGgGR7/keUILPUrkaAdLB2gIR0CkVu0VSGahdX2UKGgGR7/MLSeAd4mkaAdLBGgIR0CkVedXT3IudX2UKGgGR7/GbjLjghr4aAdLA2gIR0CkVvwOOKfndX2UKGgGR7/bvegte2NOaAdLBGgIR0CkVqd5prULdX2UKGgGR7/Y3NLUTcqOaAdLBGgIR0CkVlOh9LHudX2UKGgGR7/N26kIomXxaAdLA2gIR0CkVfZqubI+dX2UKGgGR7/LvhqCYkVvaAdLA2gIR0CkVwjvVmSRdX2UKGgGR7/EGGmDUVi4aAdLA2gIR0CkVrR5C4SZdX2UKGgGR7/U4/eLvTgEaAdLA2gIR0CkVmFJpWWAdX2UKGgGR7++fBeokzGhaAdLAmgIR0CkVnKeCkGidX2UKGgGR7/UWfbsWweOaAdLA2gIR0CkVg6FmFrVdX2UKGgGR7/CgezUqhDgaAdLA2gIR0CkVyHied08dX2UKGgGR7/QjFQ2uPmxaAdLA2gIR0CkVs3gUDdQdX2UKGgGR7/CXsw+MZP3aAdLAmgIR0CkVoFeOXE7dX2UKGgGR7/OEA5q/M4caAdLA2gIR0CkVzZGrjo7dX2UKGgGR7/JMzMzMzMzaAdLA2gIR0CkVuJiy6czdX2UKGgGR7/bYc/+sHSnaAdLBGgIR0CkVip2U0N0dX2UKGgGR7+XDBMzuWrwaAdLAWgIR0CkVjSBkI5YdX2UKGgGR7+8B2fTTfBOaAdLAmgIR0CkVvNT1kDqdX2UKGgGR7/gDklu3trsaAdLBGgIR0CkVqB7E5yVdX2UKGgGR7/Uc/dIoVmBaAdLA2gIR0CkV0/gBLf2dX2UKGgGR7/QUiY9gWrPaAdLA2gIR0CkVktT1kDqdX2UKGgGR7/K6ySmqHXVaAdLA2gIR0CkVwsunMt9dX2UKGgGR7+pD/lyR0U5aAdLAWgIR0CkVlPPkaMrdX2UKGgGR7/UhP0qYqoZaAdLA2gIR0CkV2qekHlfdX2UKGgGR7/W04R28qWkaAdLBGgIR0CkVsNITXardX2UKGgGR7/SDsMRYigTaAdLA2gIR0CkVyPC/GlzdX2UKGgGR7++PzWf9P1taAdLAmgIR0CkVtCmuTzNdX2UKGgGR7/Oadc0Ltu2aAdLA2gIR0CkVmwTVUdadX2UKGgGR7/Q37UG3WnTaAdLA2gIR0CkV4K9oN/fdX2UKGgGR7+2ndfsu3+daAdLAmgIR0CkVzY/eLvUdWUu"
+    },
+    "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:": "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",
+        "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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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbf7a4633dc88d7803b154bececfc8075535d8240e32c3866da459bf77dd9ef2
+size 45167

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7bde227ed39308337235255ae06b862be0d9507c8639d90d3eab2fd9e082b64
+size 46447

a2c-PandaReachDense-v3/pytorch_variables.pth

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

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.1.0+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 0x7fe5feeb39a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe5feeae880>"}, "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": 1697969462879586980, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-1.0531687e-01  4.6885365e-01 -2.3671322e-01]\n [ 2.5381237e-01  1.4413836e-03  4.2093030e-01]\n [-1.0586361e+00 -1.4774603e+00 -1.0739057e+00]\n [-2.1535511e-01 -1.3969159e+00 -8.9950317e-01]]", "desired_goal": "[[ 0.31808302  0.2753831  -0.3065315 ]\n [-0.26970285  0.5786891   0.5307501 ]\n [-1.4830648  -0.9312354  -0.67175347]\n [ 0.13310996 -1.3953325  -0.25097534]]", "observation": "[[-1.0531687e-01  4.6885365e-01 -2.3671322e-01 -1.8820937e+00\n   1.6185204e+00 -1.4027666e+00]\n [ 2.5381237e-01  1.4413836e-03  4.2093030e-01  4.8759189e-01\n  -4.2188829e-03  3.9118540e-01]\n [-1.0586361e+00 -1.4774603e+00 -1.0739057e+00 -7.6614118e-01\n  -9.7447717e-01 -2.3193631e-01]\n [-2.1535511e-01 -1.3969159e+00 -8.9950317e-01  1.4095596e+00\n  -8.5940725e-01 -9.3041465e-04]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.04809951  0.00277038  0.09255505]\n [ 0.08217524 -0.05426951  0.17455606]\n [ 0.10821397 -0.01851234  0.24253617]\n [-0.01285366 -0.14699642  0.09454554]]", "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:": "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", "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.0+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": -0.18233496276661754, "std_reward": 0.07001514881256109, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-22T11:08:12.638255"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:063ac4a9857ba6a0201f9b9304c056904dd2955d1a1da9e13fbb7dc0b118b557
+size 2636