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: -2.79 +/- 0.32
+      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 sha256:8a94a95146cc7b0b21faf6fcfbbeee0a897d4083d6530b196d49754818473427
+size 108011

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 0x7fd5b4c50550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd5b4c4aba0>"
+    },
+    "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": 1674326948272540509,
+    "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": "[[0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]]",
+        "desired_goal": "[[-0.70963365 -0.36167032 -0.16372637]\n [-1.0119709   0.657976    0.35073137]\n [ 1.4237516   1.5643826  -0.62721026]\n [-1.1173406  -0.35673147 -0.8758556 ]]",
+        "observation": "[[ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAlrOOvTRV/Lwm7YI+hO++vJnDFj4OqP49DbJQPTdZ/z0JQMM9tlyuPaclAb79bOs8lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "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": "[[-0.06967847 -0.03080235  0.25571555]\n [-0.02330757  0.14723052  0.12434398]\n [ 0.05095105  0.12468188  0.09533698]\n [ 0.08513777 -0.1261202   0.02873849]]",
+        "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMICDpa1ZKuAcCUhpRSlIwBbJRLMowBdJRHQKWykws5GSZ1fZQoaAZoCWgPQwiaJmw/GSMGwJSGlFKUaBVLMmgWR0Clslj4593KdX2UKGgGaAloD0MIg/bq46GvDcCUhpRSlGgVSzJoFkdApbIa9M9KVnV9lChoBmgJaA9DCKfpswOuywfAlIaUUpRoFUsyaBZHQKWx3FspG4J1fZQoaAZoCWgPQwj9ogT9hb4DwJSGlFKUaBVLMmgWR0Cls4gfEGaAdX2UKGgGaAloD0MICcOAJVeBEcCUhpRSlGgVSzJoFkdApbNNcGC7LHV9lChoBmgJaA9DCA8nMJ3WHRHAlIaUUpRoFUsyaBZHQKWzD36hxo91fZQoaAZoCWgPQwhSgCiYMQUGwJSGlFKUaBVLMmgWR0ClstEKVpsXdX2UKGgGaAloD0MIoGtfQC98CMCUhpRSlGgVSzJoFkdApbSCC4Bmw3V9lChoBmgJaA9DCMx8Bz9x4AnAlIaUUpRoFUsyaBZHQKW0Ry5I6Kd1fZQoaAZoCWgPQwjw+PauQT8HwJSGlFKUaBVLMmgWR0CltAky1uzhdX2UKGgGaAloD0MI1NSytb7I/b+UhpRSlGgVSzJoFkdApbPKnaWX1XV9lChoBmgJaA9DCFxWYTPA5QnAlIaUUpRoFUsyaBZHQKW1c5byH211fZQoaAZoCWgPQwifdvhrskYFwJSGlFKUaBVLMmgWR0CltTjAzpHJdX2UKGgGaAloD0MIeSRens7V/L+UhpRSlGgVSzJoFkdApbT620AtF3V9lChoBmgJaA9DCMzs8xjl+QHAlIaUUpRoFUsyaBZHQKW0vHUc4o91fZQoaAZoCWgPQwhngXaHFCMEwJSGlFKUaBVLMmgWR0CltmqTSsr/dX2UKGgGaAloD0MIv51EhH+xDcCUhpRSlGgVSzJoFkdApbYv2RJVbXV9lChoBmgJaA9DCBxhURGnUwbAlIaUUpRoFUsyaBZHQKW18gTyrgh1fZQoaAZoCWgPQwgoui784Dz/v5SGlFKUaBVLMmgWR0CltbPAfuCxdX2UKGgGaAloD0MI9WT+0TeJBsCUhpRSlGgVSzJoFkdApbdb83uNP3V9lChoBmgJaA9DCJmc2hmmFgTAlIaUUpRoFUsyaBZHQKW3IVafSQZ1fZQoaAZoCWgPQwhxk1FlGDf/v5SGlFKUaBVLMmgWR0CltuNIK+i8dX2UKGgGaAloD0MIWb4uw3/6FcCUhpRSlGgVSzJoFkdApbakqtozvnV9lChoBmgJaA9DCLr0L0llCgzAlIaUUpRoFUsyaBZHQKW4W+A3DN11fZQoaAZoCWgPQwhLrmLxmyIKwJSGlFKUaBVLMmgWR0CluCFPJq7AdX2UKGgGaAloD0MIs5quJ7qOB8CUhpRSlGgVSzJoFkdApbfjPfKp1nV9lChoBmgJaA9DCBHGT+Pe/P+/lIaUUpRoFUsyaBZHQKW3pKs+3Yt1fZQoaAZoCWgPQwj7c9GQ8UgDwJSGlFKUaBVLMmgWR0CluU58KG+LdX2UKGgGaAloD0MI3qrrUE1JFMCUhpRSlGgVSzJoFkdApbkToMa0hXV9lChoBmgJaA9DCNI41O/CFgzAlIaUUpRoFUsyaBZHQKW41W9US7J1fZQoaAZoCWgPQwiLcf4mFMINwJSGlFKUaBVLMmgWR0CluJbyH2ytdX2UKGgGaAloD0MIflaZKa2/BcCUhpRSlGgVSzJoFkdApbpLPSlWO3V9lChoBmgJaA9DCEeNCTGXVArAlIaUUpRoFUsyaBZHQKW6EL1mJ3x1fZQoaAZoCWgPQwicFOY9ztQIwJSGlFKUaBVLMmgWR0CludOclPaddX2UKGgGaAloD0MIh22LMhuEA8CUhpRSlGgVSzJoFkdApbmVXV9WqHV9lChoBmgJaA9DCF5m2CjrVwjAlIaUUpRoFUsyaBZHQKW7Q33pOet1fZQoaAZoCWgPQwjIQJ5dvpUDwJSGlFKUaBVLMmgWR0CluwiblRxcdX2UKGgGaAloD0MIQnv18dCnE8CUhpRSlGgVSzJoFkdApbrLlkpZwHV9lChoBmgJaA9DCBnHSPYI1QLAlIaUUpRoFUsyaBZHQKW6jZ8KG+N1fZQoaAZoCWgPQwjbUDHO38QSwJSGlFKUaBVLMmgWR0ClvD3LV4HHdX2UKGgGaAloD0MI9N2tLNGZBcCUhpRSlGgVSzJoFkdApbwC9kBjnXV9lChoBmgJaA9DCLyWkA96VgPAlIaUUpRoFUsyaBZHQKW7xNzr/sF1fZQoaAZoCWgPQwgXDK65oy8RwJSGlFKUaBVLMmgWR0Clu4Zf+jubdX2UKGgGaAloD0MI9MXeiy/6AcCUhpRSlGgVSzJoFkdApb0lQEZBLXV9lChoBmgJaA9DCC/APjp1xQrAlIaUUpRoFUsyaBZHQKW86mUnogV1fZQoaAZoCWgPQwjtYprpXkcBwJSGlFKUaBVLMmgWR0ClvKwtz0YkdX2UKGgGaAloD0MI1PIDV3mCAsCUhpRSlGgVSzJoFkdApbxtmapgkXV9lChoBmgJaA9DCBYVcTrJVv+/lIaUUpRoFUsyaBZHQKW+IQoTfzl1fZQoaAZoCWgPQwh+AihGlowEwJSGlFKUaBVLMmgWR0ClveZVOsT4dX2UKGgGaAloD0MI5KJaRBTTFcCUhpRSlGgVSzJoFkdApb2oKneiz3V9lChoBmgJaA9DCIUoX9BC4gPAlIaUUpRoFUsyaBZHQKW9aYQarFR1fZQoaAZoCWgPQwi3JXLBGbz/v5SGlFKUaBVLMmgWR0ClvyBGpda/dX2UKGgGaAloD0MIwTi4dMxZAcCUhpRSlGgVSzJoFkdApb7lpKzzE3V9lChoBmgJaA9DCDtT6LzG7g/AlIaUUpRoFUsyaBZHQKW+p6kZaV51fZQoaAZoCWgPQwhbttYXCU0FwJSGlFKUaBVLMmgWR0ClvmkYO2AodX2UKGgGaAloD0MIAKjixi3mCMCUhpRSlGgVSzJoFkdApcANb5dnkHV9lChoBmgJaA9DCEp7gy9M1hTAlIaUUpRoFUsyaBZHQKW/0pRXOnl1fZQoaAZoCWgPQwgbR6zFpyAPwJSGlFKUaBVLMmgWR0Clv5RywOe8dX2UKGgGaAloD0MImtGPhlNGDcCUhpRSlGgVSzJoFkdApb9V6Rhc7nV9lChoBmgJaA9DCDY8vVKWgQnAlIaUUpRoFUsyaBZHQKXA+WSlnAZ1fZQoaAZoCWgPQwiGOUGbHB4IwJSGlFKUaBVLMmgWR0ClwL6DwpfAdX2UKGgGaAloD0MIwF326053A8CUhpRSlGgVSzJoFkdApcCAXQ+lj3V9lChoBmgJaA9DCKbvNQTHRQPAlIaUUpRoFUsyaBZHQKXAQeLehwl1fZQoaAZoCWgPQwgjFjHsMHYRwJSGlFKUaBVLMmgWR0Clwgh1Tzd2dX2UKGgGaAloD0MIcsXFUbkJBsCUhpRSlGgVSzJoFkdApcHNvKlpGnV9lChoBmgJaA9DCACrI0c6cxDAlIaUUpRoFUsyaBZHQKXBkDIRywR1fZQoaAZoCWgPQwjcvHFSmHf5v5SGlFKUaBVLMmgWR0ClwVHR9gF5dX2UKGgGaAloD0MIdk8eFmptAcCUhpRSlGgVSzJoFkdApcMXxz7uUnV9lChoBmgJaA9DCGeZRSi2wgvAlIaUUpRoFUsyaBZHQKXC3OxB3Rp1fZQoaAZoCWgPQwjWjXdHxsoEwJSGlFKUaBVLMmgWR0Clwp7Vz6rOdX2UKGgGaAloD0MIlnuBWaHIAMCUhpRSlGgVSzJoFkdApcJg9C/oJXV9lChoBmgJaA9DCMKE0axsHwbAlIaUUpRoFUsyaBZHQKXEBX9R77d1fZQoaAZoCWgPQwhUVP1K5+MFwJSGlFKUaBVLMmgWR0Clw8q0lZ5idX2UKGgGaAloD0MI3+LhPQcmEcCUhpRSlGgVSzJoFkdApcOMm2LHdXV9lChoBmgJaA9DCMe44uKo3BHAlIaUUpRoFUsyaBZHQKXDTiFTNt91fZQoaAZoCWgPQwhMFvcfmQ4NwJSGlFKUaBVLMmgWR0ClxPnvc8DCdX2UKGgGaAloD0MIt5vgm6aPA8CUhpRSlGgVSzJoFkdApcS/Uaya/nV9lChoBmgJaA9DCNi4/l2fGQbAlIaUUpRoFUsyaBZHQKXEga4MF2V1fZQoaAZoCWgPQwhMNbOWAhIAwJSGlFKUaBVLMmgWR0ClxEN3W4EwdX2UKGgGaAloD0MIbOhmf6CcA8CUhpRSlGgVSzJoFkdApcXweii7CnV9lChoBmgJaA9DCHkkXp7OZRLAlIaUUpRoFUsyaBZHQKXFtb+Lm6p1fZQoaAZoCWgPQwiuY1xxcVQJwJSGlFKUaBVLMmgWR0ClxXekP+XJdX2UKGgGaAloD0MIhzHp76UQCcCUhpRSlGgVSzJoFkdApcU5D1Gsm3V9lChoBmgJaA9DCJ6Y9WIopwTAlIaUUpRoFUsyaBZHQKXG2cNH6M11fZQoaAZoCWgPQwiHFAMkmkAKwJSGlFKUaBVLMmgWR0Clxp7lA/s3dX2UKGgGaAloD0MIw2Fp4Ef1DsCUhpRSlGgVSzJoFkdApcZg7aIvanV9lChoBmgJaA9DCPtZLEXy1Q7AlIaUUpRoFUsyaBZHQKXGIlMRHwx1fZQoaAZoCWgPQwj0+L1Nf1YEwJSGlFKUaBVLMmgWR0Clx8/mT1TSdX2UKGgGaAloD0MItObHX1oUBcCUhpRSlGgVSzJoFkdApceVCE6DG3V9lChoBmgJaA9DCH/5ZMVwVQLAlIaUUpRoFUsyaBZHQKXHVuzhP0t1fZQoaAZoCWgPQwj8cJAQ5Yv/v5SGlFKUaBVLMmgWR0Clxxkhq0tzdX2UKGgGaAloD0MIgqs8gbBzBsCUhpRSlGgVSzJoFkdApci2qzZ6EHV9lChoBmgJaA9DCNBGrptSngDAlIaUUpRoFUsyaBZHQKXIe+JxecB1fZQoaAZoCWgPQwiUowBRMMMFwJSGlFKUaBVLMmgWR0ClyD3KbKA8dX2UKGgGaAloD0MIFOl+TkF+CcCUhpRSlGgVSzJoFkdApcf/Tw2ETXV9lChoBmgJaA9DCA6jIHh8OwzAlIaUUpRoFUsyaBZHQKXJp557gKp1fZQoaAZoCWgPQwgfhlYnZygDwJSGlFKUaBVLMmgWR0ClyWzQ/oq1dX2UKGgGaAloD0MIHAsKgzLNAcCUhpRSlGgVSzJoFkdApckursByS3V9lChoBmgJaA9DCMai6exkcAPAlIaUUpRoFUsyaBZHQKXI8CaJAMV1ZS4="
+    },
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6247043571b920af33721f0a31db57fd08685baa8657f78a1ccb59d47f857fd
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ce9dd5f2b280e93259b6f02436cf6c2acaf7294bcd4d492b364a962e49f48b8
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7fd5b4c50550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd5b4c4aba0>"}, "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": 1674326948272540509, "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": "[[0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]\n [0.37254065 0.01037557 0.41362008]]", "desired_goal": "[[-0.70963365 -0.36167032 -0.16372637]\n [-1.0119709   0.657976    0.35073137]\n [ 1.4237516   1.5643826  -0.62721026]\n [-1.1173406  -0.35673147 -0.8758556 ]]", "observation": "[[ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]\n [ 0.37254065  0.01037557  0.41362008  0.02543493 -0.00085105  0.00654826]]"}, "_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": "[[-0.06967847 -0.03080235  0.25571555]\n [-0.02330757  0.14723052  0.12434398]\n [ 0.05095105  0.12468188  0.09533698]\n [ 0.08513777 -0.1261202   0.02873849]]", "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": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.7935359733179213, "std_reward": 0.32391122074077755, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-21T19:38:37.239393"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da6aecc5783cc7d8f488881a97399b452c761ebe8f5cd2b3b34deb9ef40afb40
+size 3212