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: -2.53 +/- 0.95
+      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:e283f8dd3e3f19e6ef98108896252c3851934d5272d62bd1d8aa7413654f6434
+size 108027

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f68d4679750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f68d486a980>"
+    },
+    "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": 1685325330040545694,
+    "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": "[[0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]]",
+        "desired_goal": "[[ 0.2936134  -0.72798204 -1.0233659 ]\n [ 1.3058231  -0.04650551 -1.1266505 ]\n [-1.6413546   0.10876404  1.1931137 ]\n [-0.49342814 -0.992372   -1.6996017 ]]",
+        "observation": "[[0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]]"
+    },
+    "_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": "[[ 0.01217222 -0.0424192   0.17065965]\n [-0.10631381  0.07113529  0.15937367]\n [-0.05679473 -0.01004936  0.2603992 ]\n [-0.0029401  -0.07665523  0.1711263 ]]",
+        "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 '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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e78529084a6d96a8245d4ee86f928fef770a25c5c6eab8050128862f0ee24e0e
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:681abe7da8b1e0d711b5a851e68b020aecac005eb220301990c94fd409101cf7
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x7f68d4679750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f68d486a980>"}, "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": 1685325330040545694, "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": "[[0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]\n [0.3143867 0.0091131 0.4882923]]", "desired_goal": "[[ 0.2936134  -0.72798204 -1.0233659 ]\n [ 1.3058231  -0.04650551 -1.1266505 ]\n [-1.6413546   0.10876404  1.1931137 ]\n [-0.49342814 -0.992372   -1.6996017 ]]", "observation": "[[0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]\n [0.3143867  0.0091131  0.4882923  0.01896836 0.00056318 0.01039794]]"}, "_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": "[[ 0.01217222 -0.0424192   0.17065965]\n [-0.10631381  0.07113529  0.15937367]\n [-0.05679473 -0.01004936  0.2603992 ]\n [-0.0029401  -0.07665523  0.1711263 ]]", "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMITWpoA7AB97+UhpRSlIwBbJRLMowBdJRHQKtymrlNlAh1fZQoaAZoCWgPQwhs6jwq/u/7v5SGlFKUaBVLMmgWR0Crcl2EK3NLdX2UKGgGaAloD0MIdxGmKJcG9b+UhpRSlGgVSzJoFkdAq3Ic0SAYpHV9lChoBmgJaA9DCNZwkXu6Ova/lIaUUpRoFUsyaBZHQKtx35N47ih1fZQoaAZoCWgPQwjbNoyC4HH4v5SGlFKUaBVLMmgWR0Crc6tMfzSUdX2UKGgGaAloD0MIRRMoYhGD+r+UhpRSlGgVSzJoFkdAq3Nt8ma6SXV9lChoBmgJaA9DCGrBi76CNPi/lIaUUpRoFUsyaBZHQKtzLVf/m1Z1fZQoaAZoCWgPQwhJvhJIiZ38v5SGlFKUaBVLMmgWR0CrcvAu7HyVdX2UKGgGaAloD0MIqI/AH36+9L+UhpRSlGgVSzJoFkdAq3S6yB06o3V9lChoBmgJaA9DCA2MvKyJBQXAlIaUUpRoFUsyaBZHQKt0fW3BpHt1fZQoaAZoCWgPQwhkP4ulSH4IwJSGlFKUaBVLMmgWR0CrdDyC4BmxdX2UKGgGaAloD0MIhJ84gH5fAsCUhpRSlGgVSzJoFkdAq3P/irDIinV9lChoBmgJaA9DCLtDigESzQTAlIaUUpRoFUsyaBZHQKt1wq4pc5d1fZQoaAZoCWgPQwiSIcfWM2QIwJSGlFKUaBVLMmgWR0CrdYVBlcyFdX2UKGgGaAloD0MIGvm84qmH97+UhpRSlGgVSzJoFkdAq3VEW43FUHV9lChoBmgJaA9DCHdM3ZVdMPu/lIaUUpRoFUsyaBZHQKt1B0V8CxN1fZQoaAZoCWgPQwiHTWTmArcLwJSGlFKUaBVLMmgWR0CrdsYHxBmgdX2UKGgGaAloD0MI6gWf5uTFDcCUhpRSlGgVSzJoFkdAq3aI5NoJzHV9lChoBmgJaA9DCKzijcwjf/i/lIaUUpRoFUsyaBZHQKt2SBzV+Zx1fZQoaAZoCWgPQwgbvoV14z0BwJSGlFKUaBVLMmgWR0Crdgsqaw2VdX2UKGgGaAloD0MItAWE1sNX97+UhpRSlGgVSzJoFkdAq3fUHUtqYnV9lChoBmgJaA9DCHnou1tZIgTAlIaUUpRoFUsyaBZHQKt3ltSAH3V1fZQoaAZoCWgPQwhh+8kYH0YAwJSGlFKUaBVLMmgWR0Crd1YUN8VpdX2UKGgGaAloD0MIZ/D3i9kSCsCUhpRSlGgVSzJoFkdAq3cZJCjUNXV9lChoBmgJaA9DCLfT1ohgXPm/lIaUUpRoFUsyaBZHQKt43tdiUgV1fZQoaAZoCWgPQwjhDWlU4CT2v5SGlFKUaBVLMmgWR0CreKGA08/2dX2UKGgGaAloD0MIKLnDJjJz9r+UhpRSlGgVSzJoFkdAq3hgtBfKIXV9lChoBmgJaA9DCGXEBaBROvq/lIaUUpRoFUsyaBZHQKt4I60Y0l91fZQoaAZoCWgPQwgKTKd1G/QAwJSGlFKUaBVLMmgWR0CremM3AEdOdX2UKGgGaAloD0MIZFjFG5knAsCUhpRSlGgVSzJoFkdAq3omkcjqwHV9lChoBmgJaA9DCADhQ4mWvPq/lIaUUpRoFUsyaBZHQKt55moR7JJ1fZQoaAZoCWgPQwh9rrZif9kHwJSGlFKUaBVLMmgWR0CreapWNm16dX2UKGgGaAloD0MIPGpMiLnk/L+UhpRSlGgVSzJoFkdAq3wk3qAz6HV9lChoBmgJaA9DCJPjTulgfQHAlIaUUpRoFUsyaBZHQKt76L8aXKN1fZQoaAZoCWgPQwjnUIaqmAoBwJSGlFKUaBVLMmgWR0Cre6hvrGBGdX2UKGgGaAloD0MIoWmJldHI9r+UhpRSlGgVSzJoFkdAq3truIAOrnV9lChoBmgJaA9DCIUJo1nZPvq/lIaUUpRoFUsyaBZHQKt9yfHxSYR1fZQoaAZoCWgPQwiHGRpPBDEHwJSGlFKUaBVLMmgWR0CrfY2tlqagdX2UKGgGaAloD0MIbkxPWOLB/b+UhpRSlGgVSzJoFkdAq31OktVaOnV9lChoBmgJaA9DCDArFOl+jvq/lIaUUpRoFUsyaBZHQKt9EmNR3vB1fZQoaAZoCWgPQwjNAYI5evz9v5SGlFKUaBVLMmgWR0Crf6YJ/oaDdX2UKGgGaAloD0MIcVRuopbm77+UhpRSlGgVSzJoFkdAq39pooNNJ3V9lChoBmgJaA9DCE1p/S0BuAXAlIaUUpRoFUsyaBZHQKt/KfoRqXZ1fZQoaAZoCWgPQwjkaI6s/FIEwJSGlFKUaBVLMmgWR0Crfu4AsCkodX2UKGgGaAloD0MIrDqrBfaY+b+UhpRSlGgVSzJoFkdAq4GDVH4GlnV9lChoBmgJaA9DCK1tisdFNQrAlIaUUpRoFUsyaBZHQKuBRundfsx1fZQoaAZoCWgPQwgKTKd1GzQCwJSGlFKUaBVLMmgWR0CrgQa9TP0JdX2UKGgGaAloD0MIptHkYgxsB8CUhpRSlGgVSzJoFkdAq4DK4axX4nV9lChoBmgJaA9DCBe7fVaZCQLAlIaUUpRoFUsyaBZHQKuDg8brC3x1fZQoaAZoCWgPQwgMPPceLjn9v5SGlFKUaBVLMmgWR0Crg0dCNS62dX2UKGgGaAloD0MIhCwLJv5o9L+UhpRSlGgVSzJoFkdAq4MH8Kohp3V9lChoBmgJaA9DCOF+wAMDSPi/lIaUUpRoFUsyaBZHQKuCy2SdOIt1fZQoaAZoCWgPQwh5d2SsNl8AwJSGlFKUaBVLMmgWR0CrhUXYUWVNdX2UKGgGaAloD0MIY7g6AOJuA8CUhpRSlGgVSzJoFkdAq4UJdjXnQ3V9lChoBmgJaA9DCD0racU3FAnAlIaUUpRoFUsyaBZHQKuEyYvWYnh1fZQoaAZoCWgPQwiwcf27PnMNwJSGlFKUaBVLMmgWR0CrhI00WM0hdX2UKGgGaAloD0MIkNlZ9E7lBMCUhpRSlGgVSzJoFkdAq4caaRZED3V9lChoBmgJaA9DCCnMe5xpwve/lIaUUpRoFUsyaBZHQKuG3mQr+YN1fZQoaAZoCWgPQwhcd/NUhzwGwJSGlFKUaBVLMmgWR0Crhp8GcFyJdX2UKGgGaAloD0MIYVRSJ6CJAMCUhpRSlGgVSzJoFkdAq4ZjLKV6eHV9lChoBmgJaA9DCORLqODwQvS/lIaUUpRoFUsyaBZHQKuI+2gFotd1fZQoaAZoCWgPQwgrTUpBt9fwv5SGlFKUaBVLMmgWR0CriL75Ec81dX2UKGgGaAloD0MIINCZtKk6+b+UhpRSlGgVSzJoFkdAq4h+dd3Sr3V9lChoBmgJaA9DCKWeBaG8TwfAlIaUUpRoFUsyaBZHQKuIQahHskZ1fZQoaAZoCWgPQwhlbynniz36v5SGlFKUaBVLMmgWR0CrihDPWxyGdX2UKGgGaAloD0MIZ0eq7/wi9r+UhpRSlGgVSzJoFkdAq4nT4YaYNXV9lChoBmgJaA9DCECEuHL2zg/AlIaUUpRoFUsyaBZHQKuJlo4dZJV1fZQoaAZoCWgPQwjlRpG1hhL0v5SGlFKUaBVLMmgWR0CriVo9LYf5dX2UKGgGaAloD0MIjs75KY7DAMCUhpRSlGgVSzJoFkdAq4sdaOgg5nV9lChoBmgJaA9DCMtHUtLDkPa/lIaUUpRoFUsyaBZHQKuK4B19v0h1fZQoaAZoCWgPQwiwHCEDeXb9v5SGlFKUaBVLMmgWR0Crip+67NB4dX2UKGgGaAloD0MIngjiPJxA/r+UhpRSlGgVSzJoFkdAq4pihN/OMXV9lChoBmgJaA9DCPW4b7VOHAbAlIaUUpRoFUsyaBZHQKuMI7W/ag51fZQoaAZoCWgPQwhORpVh3M0MwJSGlFKUaBVLMmgWR0Cri+agM+eOdX2UKGgGaAloD0MIPQytTs5QAsCUhpRSlGgVSzJoFkdAq4umDDjzZ3V9lChoBmgJaA9DCM2RlV8GwwzAlIaUUpRoFUsyaBZHQKuLaPczqKR1fZQoaAZoCWgPQwhQwkzbvzL6v5SGlFKUaBVLMmgWR0CrjTE1l5GCdX2UKGgGaAloD0MIBHCzeLHgEMCUhpRSlGgVSzJoFkdAq4zz7Q9idHV9lChoBmgJaA9DCHyZKELq9v+/lIaUUpRoFUsyaBZHQKuMsyB06o51fZQoaAZoCWgPQwgoRwGiYGYBwJSGlFKUaBVLMmgWR0CrjHX49HMEdX2UKGgGaAloD0MI6L6c2a4QCsCUhpRSlGgVSzJoFkdAq45daSs8xXV9lChoBmgJaA9DCC0LJv4oavG/lIaUUpRoFUsyaBZHQKuOIOrhisp1fZQoaAZoCWgPQwgjgnFw6bgBwJSGlFKUaBVLMmgWR0CrjeApjMFEdX2UKGgGaAloD0MIuRyvQPQkBcCUhpRSlGgVSzJoFkdAq42i7yxzJnV9lChoBmgJaA9DCDIAVHHjVgHAlIaUUpRoFUsyaBZHQKuPkuJUHY91fZQoaAZoCWgPQwiqRq8GKA39v5SGlFKUaBVLMmgWR0Crj1XEIgNgdX2UKGgGaAloD0MIpMaEmEvKAMCUhpRSlGgVSzJoFkdAq48U+7lJYnV9lChoBmgJaA9DCGFQptHkIgTAlIaUUpRoFUsyaBZHQKuO18wYced1fZQoaAZoCWgPQwiWXTC45g4MwJSGlFKUaBVLMmgWR0CrkKDIBBAwdX2UKGgGaAloD0MICMiXUMGh+b+UhpRSlGgVSzJoFkdAq5BjlA/s3XV9lChoBmgJaA9DCGU08nnFAxDAlIaUUpRoFUsyaBZHQKuQIre67NB1fZQoaAZoCWgPQwg7j4r/O2IOwJSGlFKUaBVLMmgWR0Crj+XLNfPYdX2UKGgGaAloD0MIx/MZUG+GBMCUhpRSlGgVSzJoFkdAq5GkEgW8AnV9lChoBmgJaA9DCIeIm1PJAAXAlIaUUpRoFUsyaBZHQKuRZsdkrgB1fZQoaAZoCWgPQwiSzVXzHJEBwJSGlFKUaBVLMmgWR0CrkSX8n/kvdX2UKGgGaAloD0MI097gC5OpAcCUhpRSlGgVSzJoFkdAq5Do3DNyHXV9lChoBmgJaA9DCDyfAfVmlPG/lIaUUpRoFUsyaBZHQKuS1fwZwXJ1fZQoaAZoCWgPQwip+pXOh6f9v5SGlFKUaBVLMmgWR0CrkpifpUxVdX2UKGgGaAloD0MIyEPf3cpyCsCUhpRSlGgVSzJoFkdAq5JYSi/O+3V9lChoBmgJaA9DCFxXzAhv7xDAlIaUUpRoFUsyaBZHQKuSG7W/ag51ZS4="}, "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 '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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.5327268681954593, "std_reward": 0.9510243205352475, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-29T02:54:15.223355"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0da5892a726b2dd968d716162f45c0919cb85ca19129f9681477b9e35b2e6b09
+size 2470