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: -2.15 +/- 0.31
+      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 sha256:6f2c7154e0247c7ba06f24fcd7660693a736158551c3213fb28c40a9db4e3a6d
+size 108046

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 0x7f5fc7a661f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f5fc7a65380>"
+    },
+    "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": 1681481052325756418,
+    "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": "[[0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]]",
+        "desired_goal": "[[ 1.3294978   1.2492396   0.53866035]\n [-0.12845257 -0.09572888 -0.19958174]\n [ 0.5439338   1.4356194   0.04335233]\n [ 1.2564515  -1.4648464  -1.3436092 ]]",
+        "observation": "[[ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]]"
+    },
+    "_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": "[[ 0.01590043 -0.07270119  0.15345488]\n [-0.00398203  0.11501727  0.2965495 ]\n [-0.13568415  0.10273009  0.22259793]\n [ 0.02169541  0.04779077  0.02989576]]",
+        "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////9LAHSUYkMIoIzxYfZy/7+UhpRSlIwBbJRLMowBdJRHQKXU97+kxh51fZQoaAZoCWgPQwgIc7uX+6T7v5SGlFKUaBVLMmgWR0Cl1HxW1c+rdX2UKGgGaAloD0MI3gAz38EP/b+UhpRSlGgVSzJoFkdApdQaIk7fYXV9lChoBmgJaA9DCGSsNv+vWgXAlIaUUpRoFUsyaBZHQKXTrOoHcDd1fZQoaAZoCWgPQwhmguFcw2wAwJSGlFKUaBVLMmgWR0Cl1mQyRB/rdX2UKGgGaAloD0MI4xx1dFxN+r+UhpRSlGgVSzJoFkdApdXpCBwuNHV9lChoBmgJaA9DCHrjpDDvkQHAlIaUUpRoFUsyaBZHQKXVhs2NvO11fZQoaAZoCWgPQwi9xcN7DuwAwJSGlFKUaBVLMmgWR0Cl1RmyPdVOdX2UKGgGaAloD0MIhslUwajEAcCUhpRSlGgVSzJoFkdApdfbKgZjx3V9lChoBmgJaA9DCM43onvWFQHAlIaUUpRoFUsyaBZHQKXXX88cMmZ1fZQoaAZoCWgPQwhYN94dGWsBwJSGlFKUaBVLMmgWR0Cl1v3/5tWNdX2UKGgGaAloD0MIzQGCOXq89r+UhpRSlGgVSzJoFkdApdaQ4ffXPXV9lChoBmgJaA9DCAH20akr3/+/lIaUUpRoFUsyaBZHQKXZT27nPmh1fZQoaAZoCWgPQwjqswOuK+YJwJSGlFKUaBVLMmgWR0Cl2NP24/eMdX2UKGgGaAloD0MIfZQRF4DGDMCUhpRSlGgVSzJoFkdApdhx5iVjZ3V9lChoBmgJaA9DCNuJkpBI2wLAlIaUUpRoFUsyaBZHQKXYBJvHcUN1fZQoaAZoCWgPQwiWd9UD5qEHwJSGlFKUaBVLMmgWR0Cl2sFf7aZhdX2UKGgGaAloD0MI2c9iKZLv/L+UhpRSlGgVSzJoFkdApdpGLcbiqHV9lChoBmgJaA9DCGK85lWd1QXAlIaUUpRoFUsyaBZHQKXZ5DtPYWd1fZQoaAZoCWgPQwgdrP9zmE8AwJSGlFKUaBVLMmgWR0Cl2XcQiA2AdX2UKGgGaAloD0MI/mDgufcwAsCUhpRSlGgVSzJoFkdApdxH0TURWnV9lChoBmgJaA9DCJLM6h1uh/i/lIaUUpRoFUsyaBZHQKXbzGEwnIB1fZQoaAZoCWgPQwigF+5cGCkNwJSGlFKUaBVLMmgWR0Cl22o371qWdX2UKGgGaAloD0MIOgMjL2siB8CUhpRSlGgVSzJoFkdApdr9IGyHEnV9lChoBmgJaA9DCOQR3EjZ4gjAlIaUUpRoFUsyaBZHQKXdWhQm/nJ1fZQoaAZoCWgPQwj430p2bOQEwJSGlFKUaBVLMmgWR0Cl3N4tg8bJdX2UKGgGaAloD0MIRRFSt7MvBsCUhpRSlGgVSzJoFkdApdx7hR64UnV9lChoBmgJaA9DCJiFdk6zwATAlIaUUpRoFUsyaBZHQKXcDe67NB51fZQoaAZoCWgPQwgFw7mGGVr8v5SGlFKUaBVLMmgWR0Cl3jtFjNILdX2UKGgGaAloD0MI0qxsH/KWAMCUhpRSlGgVSzJoFkdApd2/RmbsnnV9lChoBmgJaA9DCOMYyR6hJvu/lIaUUpRoFUsyaBZHQKXdXLNfPX11fZQoaAZoCWgPQwhS8uocAzIBwJSGlFKUaBVLMmgWR0Cl3O7YTTOPdX2UKGgGaAloD0MIh272B8rNBMCUhpRSlGgVSzJoFkdApd8cM/hVEXV9lChoBmgJaA9DCOrMPSR8jwXAlIaUUpRoFUsyaBZHQKXeoDaoMrp1fZQoaAZoCWgPQwjBN02fHbD7v5SGlFKUaBVLMmgWR0Cl3j2T5ftydX2UKGgGaAloD0MI5+CZ0CQxBMCUhpRSlGgVSzJoFkdApd3P2bobGXV9lChoBmgJaA9DCNMwfERMSQXAlIaUUpRoFUsyaBZHQKXgAnCO3lV1fZQoaAZoCWgPQwgTglX18vv9v5SGlFKUaBVLMmgWR0Cl34aBAfMfdX2UKGgGaAloD0MIBCDu6lWk/r+UhpRSlGgVSzJoFkdApd8jyz5XVHV9lChoBmgJaA9DCKLrwg/OJwPAlIaUUpRoFUsyaBZHQKXetjuKGcp1fZQoaAZoCWgPQwgWS5F8JTADwJSGlFKUaBVLMmgWR0Cl4NwHiWE9dX2UKGgGaAloD0MIvJF55A8GBsCUhpRSlGgVSzJoFkdApeBgEW69TXV9lChoBmgJaA9DCNuizAaZpP2/lIaUUpRoFUsyaBZHQKXf/V3ljmV1fZQoaAZoCWgPQwhC7bd2osQGwJSGlFKUaBVLMmgWR0Cl34+aa1CxdX2UKGgGaAloD0MIVaUtrvH5BMCUhpRSlGgVSzJoFkdApeG+Wa+ev3V9lChoBmgJaA9DCHXN5JttzgDAlIaUUpRoFUsyaBZHQKXhQoRZlnR1fZQoaAZoCWgPQwimtz8XDXkFwJSGlFKUaBVLMmgWR0Cl4N/+0gKXdX2UKGgGaAloD0MIniPyXUodBMCUhpRSlGgVSzJoFkdApeByKUFB6nV9lChoBmgJaA9DCMqJdhVSfgHAlIaUUpRoFUsyaBZHQKXinCMxXXB1fZQoaAZoCWgPQwguH0lJD0MJwJSGlFKUaBVLMmgWR0Cl4iAfU4JedX2UKGgGaAloD0MI/wkuVtSAAsCUhpRSlGgVSzJoFkdApeG9g0CRwXV9lChoBmgJaA9DCBUaiGUzBwvAlIaUUpRoFUsyaBZHQKXhT7XxvvV1fZQoaAZoCWgPQwiqYb8n1qn7v5SGlFKUaBVLMmgWR0Cl43dcbBGhdX2UKGgGaAloD0MIuW5Kea3EAsCUhpRSlGgVSzJoFkdApeL7Ta0x/XV9lChoBmgJaA9DCI84ZAPpogrAlIaUUpRoFUsyaBZHQKXimKUmlZZ1fZQoaAZoCWgPQwg2Ia0x6CQJwJSGlFKUaBVLMmgWR0Cl4irPUrkKdX2UKGgGaAloD0MIKelhaHUyBMCUhpRSlGgVSzJoFkdApeRYqTbFj3V9lChoBmgJaA9DCIqT+x2KwhDAlIaUUpRoFUsyaBZHQKXj3JnQID51fZQoaAZoCWgPQwhgkzXqIXoGwJSGlFKUaBVLMmgWR0Cl43nSF49pdX2UKGgGaAloD0MIVvXyO01GBsCUhpRSlGgVSzJoFkdApeMMAFPi1nV9lChoBmgJaA9DCAZINIEiNgHAlIaUUpRoFUsyaBZHQKXlPX4CZF51fZQoaAZoCWgPQwhblq/L8B8AwJSGlFKUaBVLMmgWR0Cl5MGG/N7jdX2UKGgGaAloD0MIZavLKQGx/L+UhpRSlGgVSzJoFkdApeRfCTEBKnV9lChoBmgJaA9DCGObVDTW/gDAlIaUUpRoFUsyaBZHQKXj8THsC1Z1fZQoaAZoCWgPQwjZJ4BiZIn/v5SGlFKUaBVLMmgWR0Cl5i3oC+10dX2UKGgGaAloD0MIz04GR8lLA8CUhpRSlGgVSzJoFkdApeWyFK02L3V9lChoBmgJaA9DCB8tzhjmhAPAlIaUUpRoFUsyaBZHQKXlT3evZAZ1fZQoaAZoCWgPQwiLUdfa+zQPwJSGlFKUaBVLMmgWR0Cl5OHpKSPmdX2UKGgGaAloD0MIC+9yEd8pAcCUhpRSlGgVSzJoFkdApecjAk9lmXV9lChoBmgJaA9DCI/66xUWfADAlIaUUpRoFUsyaBZHQKXmpxaPjn51fZQoaAZoCWgPQwgeigJ9Ik8EwJSGlFKUaBVLMmgWR0Cl5kRe9i+ddX2UKGgGaAloD0MILv8h/fa1BcCUhpRSlGgVSzJoFkdApeXWqvNeMXV9lChoBmgJaA9DCJUO1v85TALAlIaUUpRoFUsyaBZHQKXoFnpSrHV1fZQoaAZoCWgPQwgVdHtJYxQBwJSGlFKUaBVLMmgWR0Cl55p++dsjdX2UKGgGaAloD0MIfshbrn4sBcCUhpRSlGgVSzJoFkdApec33FkxynV9lChoBmgJaA9DCJ5haksd5P+/lIaUUpRoFUsyaBZHQKXmyjQiRnx1fZQoaAZoCWgPQwgVjiCVYgf4v5SGlFKUaBVLMmgWR0Cl6Px0+1SgdX2UKGgGaAloD0MIyD8ziA/sCMCUhpRSlGgVSzJoFkdApeiAY+B6KXV9lChoBmgJaA9DCP4PsFbt+gbAlIaUUpRoFUsyaBZHQKXoHZQHiWF1fZQoaAZoCWgPQwhA+FCiJc8HwJSGlFKUaBVLMmgWR0Cl56+8f3evdX2UKGgGaAloD0MIkh/xK9aQCsCUhpRSlGgVSzJoFkdApenXJtBOYnV9lChoBmgJaA9DCDtSfecXpQTAlIaUUpRoFUsyaBZHQKXpWxfv4M51fZQoaAZoCWgPQwhOKa+V0N0BwJSGlFKUaBVLMmgWR0Cl6PhS9/SZdX2UKGgGaAloD0MIFQFO7+I9AsCUhpRSlGgVSzJoFkdApeiKlrM1THV9lChoBmgJaA9DCGyWy0bnnAvAlIaUUpRoFUsyaBZHQKXqvLamGdt1fZQoaAZoCWgPQwgxW7Iqwq0HwJSGlFKUaBVLMmgWR0Cl6kDs2NvPdX2UKGgGaAloD0MI5WGh1jQ/EcCUhpRSlGgVSzJoFkdApeneHvc8DHV9lChoBmgJaA9DCLWK/tDMUwrAlIaUUpRoFUsyaBZHQKXpcFGoaUB1fZQoaAZoCWgPQwjcSxqjdZQHwJSGlFKUaBVLMmgWR0Cl65e49X9zdX2UKGgGaAloD0MIKnEd44oLCcCUhpRSlGgVSzJoFkdApesbqfOD8XV9lChoBmgJaA9DCG6mQjwSLwfAlIaUUpRoFUsyaBZHQKXquPDHfdh1fZQoaAZoCWgPQwi858ByhEz5v5SGlFKUaBVLMmgWR0Cl6ksPJ7swdX2UKGgGaAloD0MIsKnzqPj//7+UhpRSlGgVSzJoFkdApeyA00m+kHV9lChoBmgJaA9DCII2OXzSCQTAlIaUUpRoFUsyaBZHQKXsBPE87p51fZQoaAZoCWgPQwjpnnWNlkMDwJSGlFKUaBVLMmgWR0Cl66Io3JgcdX2UKGgGaAloD0MIs3xdhv+0/b+UhpRSlGgVSzJoFkdApes0WsRxtHV9lChoBmgJaA9DCKuVCb/UrwDAlIaUUpRoFUsyaBZHQKXtkAVfu1F1fZQoaAZoCWgPQwhrfZHQlvP+v5SGlFKUaBVLMmgWR0Cl7RQjt5UtdX2UKGgGaAloD0MIYi8UsB2MA8CUhpRSlGgVSzJoFkdApeyydc0Lt3V9lChoBmgJaA9DCCZV203w7QbAlIaUUpRoFUsyaBZHQKXsRNxEORV1ZS4="
+    },
+    "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:": "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9fd928b7e811a5b58dffc6cff8f394368540946f13dafd1a062e2e147293c9e
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40af714b00a20fb07d632bf19acd94e12188918ddce90076ff7b06208030d16e
+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: 1.8.0
+- PyTorch: 2.0.0+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 0x7f5fc7a661f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5fc7a65380>"}, "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": 1681481052325756418, "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": "[[0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]\n [0.38269055 0.0039443  0.5355887 ]]", "desired_goal": "[[ 1.3294978   1.2492396   0.53866035]\n [-0.12845257 -0.09572888 -0.19958174]\n [ 0.5439338   1.4356194   0.04335233]\n [ 1.2564515  -1.4648464  -1.3436092 ]]", "observation": "[[ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]\n [ 0.38269055  0.0039443   0.5355887  -0.00632396  0.00170728 -0.00679116]]"}, "_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": "[[ 0.01590043 -0.07270119  0.15345488]\n [-0.00398203  0.11501727  0.2965495 ]\n [-0.13568415  0.10273009  0.22259793]\n [ 0.02169541  0.04779077  0.02989576]]", "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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.146421296149492, "std_reward": 0.31209890473047586, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-14T14:51:02.339984"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05e732c3880cc294399f64ad83fa40fe1b3c081e688314179272241e28581d4c
+size 2381