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: TQC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -0.20 +/- 0.11
+      name: mean_reward
+      verified: false
+---
+
+# **TQC** Agent playing **PandaReachDense-v2**
+This is a trained model of a **TQC** 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
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "sb3_contrib.tqc.policies", "__doc__": "\n    Policy class (with both actor and critic) for TQC.\n\n    :param observation_space: Observation space\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\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    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ", "__init__": "<function MultiInputPolicy.__init__ at 0x7fb60b7c3c10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb60b7bfc30>"}, "verbose": 1, "policy_kwargs": {"use_sde": 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:": "gAWV9QsAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UjBRudW1weS5yYW5kb20uX3BpY2tsZZSMEl9fcmFuZG9tc3RhdGVfY3RvcpSTlIwHTVQxOTkzN5SFlFKUfZQojA1iaXRfZ2VuZXJhdG9ylGgwjAVzdGF0ZZR9lCiMA2tleZRoEiiWwAkAAAAAAACXFSpHCrtGYIgoCMAn3rYcoC/Me2r3ePl+rKS5cl2VHdnzzdrJKcldU6SXf9tgCobM9SxTFCYC3zwyFuGnP5cHfKpRyfNOhYBHywm/N7+vtU6UUktgGEsg+l9zLX2LqdZ8ZzqNk31vR88d0J9c2eI9Yoi0i9S/9/GN92HrrOB9v/soRoU7B3SbWd4TQ4Vx05uQnpwykhWoDEozZdEjbmCeK20tTdh7GDcJcKDfc5AnKVJegPjHy+QLOtDPtxd/EHbYjTD7fikEa2oj7uPyJqT7g1riuWv0DcH4h1kHfYz9W04uB5Pb+DwxbaiX2pTDJXbjBO+uSfw4+ujIT6cY4iw7Zt5fY222eMHZYH1FBX8QxpUFpenp1/sCQDRn6dkeRigYLSXOEltEGHkVophC8ltApubZAvuQ4K+tyS8IAjZymuLHlKQmzdw+SfowKTzmiSSeW++jGZKLqkqOVOlzkrb0Q5A8q/JR4g/sbdNSY39JkHjvRy5k8b4dsBhgi/LFk6rrtV+U81ENOYbPfNyWsFScnkuf27O30C4XsEbJ1wrSU1GDVHZkOzkfrypUWDKG1NCeVVHaTjtk0GotY2u3GCj5w4EQrjqUlXPKtvo6EdYq4BWquW9p4XNeTXB81CQRq1O9bJ0EAsiv0OFdT+xAEguKfm4NW0Mj2Meq8S3p8E5XnqpvOAzXEQxBjxX2Iyq4gVi925M3orB3/U42IyQZoD/qn7N9hxsu5cr2oQLY4Cs0myDvMpA2WNC2QNufKFkF6x+4pxjO4ZDloxLfEFYMP14JQtdDK1e7ohaKMqSq9r2YLOV9LuMkmVgNNMjo+cpYHkz0qQkckvZZijSjwWzSwEVZjdPoF24uex4algCkTg6DPrjEwk+ryHebvUvI0LjuziopQkjghldegc+TIsf1c9XPy3sf6eCRIJDbbSjHxAI/SyjASBhmm6AUVTAo/OmSl5flBkscCZzWDxOeePkCqNcAgGr8r5fZgKoIrE7rM8Yb6ohKxmapiB7AvgqJH/ZptNzEsS04JzjiBGKZKPJ1QPGfV6oeX6FVTLNDUPn/kqOM03XWZBWU2LX45ffSGMcvcNM0TsMerygJqnb9YuIKgKdG3LCUX0riFytuFw3rk+Vbba1pBDDMbwSNDfFBBGIo/DDSIRQxX3zRUJE9l8XsDt2qr7z8NnKQre3QjTLNZiQSFyGuROL/P+1miIXgnKhMUJoaLukBKmgIjj+GJSimvOsP49Rn5GLt5aV1vLxeN7vc2kWD3vTERCfnF8JRcGCIxIltzMPK7obNfZpsIRwUDd+QUYRb0K2Ku1XvkJSurjeA2sVUO0q3j2Si1rXm5HtttgqHCiV8LegIzt5zXfDDNRP7NRtik2D7RONDAetth7D7zdGaSV9RwokDL5FkQxaJyjWeCXWcS68Q+PjcsXKEsqPLBfdpwdHTwlbA1v95crBr7nUvDYIsbDy1owMtZmhs+Mf5B3VIsRCmLa4xHFalCdMCwRWgAVyv8/73HiTkBtdz+fw3lKot2xrSROUzWuHii1rPcl/NNMF+fVi887RmBaq0AYD7l8VrRuKK/vICeH9ouh+w8Xz8jSXs3K5RB+uQZxVjmKuNtd6EcfnXn84sQBSiVEilwxXZvNfrjTQg6b+RWQ2zkzcbBdr7XAZbFXuaKEFAXQSW9B2xngV6ydvSMTubDuaguRySXZhNwkX+oY9Bbld+nUkEWKjkTfVLfjM+TgQHRQEU9HgSrp8Y96CIcU1ngxayAdoA4WEboH6tA6zaTXbcHDsF8AWNUBVuqVfJ2Lzs33lISehzH/gQy3BbjbSXDBdRv/w5zGYHGlwS5o8inHWqqe9ga2Qs1c7gDFC/fx1+6FaU0Jvwexyd7wmumwUhOIy6mpsJdS3yyozgatpISG/f6Yb08LBu2+1gTwGx0/5Hdo/FnWVhWtsjcy+xBCBYbrH8LsVOmb+6cy5C7A6miqlS6zLfkIl4UPU9KzjS4ZIENiQY/ESy8Z9G1utniw6eWD6e+2BNWDB5xt4t+ftCqO0Yj/aG5RAKHlfw7rTihEixobfTMw8pmZcLEYZ8s5GtoTTyVI42gsJaIgEBXdw1bVw6MNU9bhe3RSRIX52s28DuLwbMdv9Q5Okl2LfN3xX4eHLPjfYXCJAmenVNxzsTw3Ez9qX1kSyuI0aLvUX5nnCoHyMg8AnVSeZqR6ipqo8dUE7Te4LRLBUBpRUUQK2nNrDKugol7dVQ8jGu8rP6JiNIKwj5DvO6j785sWPdHVi+azc+nDQvn+rFSCuonbbKDLVj442WWLHSCyxZqW59PdrPM8f9E3+av9KpIDATfhoVxRikLvBzODpHV5F9vN7NO958fRknlfeaz1Q2SOGxZl2GeUTSpQqbkgpDl25AY3R0UBxMGSE4BfgOeR9F1R/ZJejyP6LyyV74625NU8YxhBIoAbVtDOHbvm3asF62+01HCZcmpaZ0SYWWHAsy1I0CMVPW8ig90ftN0LdAJ07HG9IeQwvUjJsgldP05BHk7TuNFwWobxGnkF3EB2IhU2uY8TeqU1QQbYZZF3kitNBnnQOU9taFJxZWWP0c+nXNtrPkSCf+2sfxzbDBrHvIL/jYLaAqUqw/fFu1+utasLipXyY3HIGtheNk1KzcTlAaIOKpxckh8ve8Drgsji1aqAGGfapMQEa/Cf/IVkrjpsHBuK2izJ7cvRbiyNt5kOb7Tq5ygarW7PHMu17jwTZWGegn9r89lRnRZPyVPJtEGu3VmvFMLRIrSgRY4KC03Hcp+VIuGi67zu23eMqsB2Z9hy+Q9reRDqJoht8INGpqX/imwvt9vTefe5yNe80fsT/CVo9GZjUmrTa/1VAxo/9YIr95icXYOah8A5jBcpDJsRiUP7VwYKyx081RL9eheCViPiUuy1m5ZmlbZFtnC+ofNZSMAuKQtBqa+4G7SW2Cn/ptY8ZiDEQtFSkFmu12GN5+SPV856C6OSVA2zDd6nd07ThG6WcW9IRHNY2WUuVW1F8eZHObBTmRPhyrusF9uBsEPC6zpB8rrCb8djtrxDHfx8XIs0nc0kgWgnDLQZ4dPwl+SWj9hxOo5rmvLo/Kl8A+B1lp/unVk4LGewgWQsgARpdj+0wrD8EQ2oGRMWoYaoQJugX8yb+rQge014++zb1iaV7E5tVEWz4AXJDFIaAA3cw4q7eY8gS0QEyYaNyknd3iQEPFJw+n+zrp1lYGJPH+4U3datVbacDr52tYwT8weoMurSlGjYoWtaN1TV8TzG8Q0ywANZrsxtGuefBdEFVIMZrCpyaUbWvYfcUJ0FIxEGlk66HEN56T1YCbOVxS4/qVm3aUaAeMAnU0lImIh5RSlChLA2gLTk5OSv////9K/////0sAdJRiTXAChZRoFXSUUpSMA3Bvc5RNWAJ1jAloYXNfZ2F1c3OUSwCMBWdhdXNzlEcAAAAAAAAAAHVidWIu", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": "RandomState(MT19937)"}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674396849105999628, "learning_rate": 0.001, "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": "[[0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]]", "desired_goal": "[[ 0.76670194 -0.356108    0.685178  ]\n [ 0.6586274  -0.8357658   0.42670015]\n [-0.71320754 -0.85692686 -0.4155283 ]\n [ 1.7247589   1.0068423  -1.0498981 ]]", "observation": "[[ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]]"}, "_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": "[[ 0.06641828 -0.03104412  0.21035875]\n [ 0.05702582 -0.07258389  0.18791056]\n [-0.0621965  -0.0744165   0.11476498]\n [ 0.1496803   0.08699138  0.05967144]]", "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": 20000, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI4dBbPLzn07+UhpRSlIwBbJRLMowBdJRHQLyrlp4rz5J1fZQoaAZoCWgPQwgAjj17LlPWv5SGlFKUaBVLMmgWR0C8q3krf+CLdX2UKGgGaAloD0MIHlN3ZRcM27+UhpRSlGgVSzJoFkdAvKta5OJtSHV9lChoBmgJaA9DCGU5CaUvBOG/lIaUUpRoFUsyaBZHQLyrNiMYMv11fZQoaAZoCWgPQwiqRNlbyvnTv5SGlFKUaBVLMmgWR0C8rR+mm+CcdX2UKGgGaAloD0MI7gbRWtHm5L+UhpRSlGgVSzJoFkdAvK0CVTrE+HV9lChoBmgJaA9DCDyjrUoi+9m/lIaUUpRoFUsyaBZHQLys5A4GUwB1fZQoaAZoCWgPQwiE8dO4N7/dv5SGlFKUaBVLMmgWR0C8rL9YjjaPdX2UKGgGaAloD0MIvXK9baZC2L+UhpRSlGgVSzJoFkdAvK6lJ4B3inV9lChoBmgJaA9DCLDIrx9iA+W/lIaUUpRoFUsyaBZHQLyuh5vcafl1fZQoaAZoCWgPQwjEsS5uowHjv5SGlFKUaBVLMmgWR0C8rmla8pTddX2UKGgGaAloD0MIks7AyMua1b+UhpRSlGgVSzJoFkdAvK5EqlP8AXV9lChoBmgJaA9DCNl22hoRDOG/lIaUUpRoFUsyaBZHQLywF9eQdS51fZQoaAZoCWgPQwh3n+OjxZngv5SGlFKUaBVLMmgWR0C8r/pZr56/dX2UKGgGaAloD0MIymsldJfE1L+UhpRSlGgVSzJoFkdAvK/cH7gsLHV9lChoBmgJaA9DCD2elh+4SuC/lIaUUpRoFUsyaBZHQLyvt3Kji4t1fZQoaAZoCWgPQwiBtP8B1qrgv5SGlFKUaBVLMmgWR0C8sZFG9YfXdX2UKGgGaAloD0MICmXh62td1r+UhpRSlGgVSzJoFkdAvLFz029+PXV9lChoBmgJaA9DCI85z9iXbOO/lIaUUpRoFUsyaBZHQLyxVaOPvKF1fZQoaAZoCWgPQwg3GsBbIEHSv5SGlFKUaBVLMmgWR0C8sTDfJmuldX2UKGgGaAloD0MIuYybGmg+37+UhpRSlGgVSzJoFkdAvLMEj5bhWHV9lChoBmgJaA9DCMjNcAM+P9C/lIaUUpRoFUsyaBZHQLyy5xxkupV1fZQoaAZoCWgPQwh+Vpkprb/bv5SGlFKUaBVLMmgWR0C8ssjNt65YdX2UKGgGaAloD0MIjjulg/V/1b+UhpRSlGgVSzJoFkdAvLKkI3R5T3V9lChoBmgJaA9DCAGFevoI/OC/lIaUUpRoFUsyaBZHQLy0b4/eLvV1fZQoaAZoCWgPQwhQptHkYgzgv5SGlFKUaBVLMmgWR0C8tFIJ3PiUdX2UKGgGaAloD0MIjSRBuAIK17+UhpRSlGgVSzJoFkdAvLQz1lGwzXV9lChoBmgJaA9DCIXSF0LO+9y/lIaUUpRoFUsyaBZHQLy0DyFfzBh1fZQoaAZoCWgPQwjK+ziaIyvUv5SGlFKUaBVLMmgWR0C8td5t78ekdX2UKGgGaAloD0MIcqWeBaG83r+UhpRSlGgVSzJoFkdAvLXA7tAs1HV9lChoBmgJaA9DCJKXNbHA1+C/lIaUUpRoFUsyaBZHQLy1oq/dqL11fZQoaAZoCWgPQwguxsA6jh/dv5SGlFKUaBVLMmgWR0C8tX384xUOdX2UKGgGaAloD0MIZoUi3c8p3r+UhpRSlGgVSzJoFkdAvLdR9hJAdHV9lChoBmgJaA9DCJLrppTXStq/lIaUUpRoFUsyaBZHQLy3NIfbKzR1fZQoaAZoCWgPQwhPPj22ZcDVv5SGlFKUaBVLMmgWR0C8txZccENfdX2UKGgGaAloD0MIbqZCPBKv4b+UhpRSlGgVSzJoFkdAvLbxrvb48HV9lChoBmgJaA9DCIpamlshrN2/lIaUUpRoFUsyaBZHQLy4xaWHDaZ1fZQoaAZoCWgPQwhckgN2NXniv5SGlFKUaBVLMmgWR0C8uKguyu6mdX2UKGgGaAloD0MIucK7XMR31b+UhpRSlGgVSzJoFkdAvLiJ7kXDWXV9lChoBmgJaA9DCCQrvwzGiNK/lIaUUpRoFUsyaBZHQLy4ZTP0I1N1fZQoaAZoCWgPQwjymld1VgvTv5SGlFKUaBVLMmgWR0C8ujbmZE2HdX2UKGgGaAloD0MIhpLJqZ1h3L+UhpRSlGgVSzJoFkdAvLoZyZKFqXV9lChoBmgJaA9DCMVVZd8VQeC/lIaUUpRoFUsyaBZHQLy5+/0NBnl1fZQoaAZoCWgPQwjOGryvyoXZv5SGlFKUaBVLMmgWR0C8udhHskY5dX2UKGgGaAloD0MIHm6HhsWo3b+UhpRSlGgVSzJoFkdAvLuqh9LHuXV9lChoBmgJaA9DCMpQFVPpJ96/lIaUUpRoFUsyaBZHQLy7jQaJhv11fZQoaAZoCWgPQwhSQxuADQjiv5SGlFKUaBVLMmgWR0C8u265Xlr/dX2UKGgGaAloD0MIUKbR5GIM0b+UhpRSlGgVSzJoFkdAvLtJ+1Bt13V9lChoBmgJaA9DCOmbNA2KZuC/lIaUUpRoFUsyaBZHQLy9GRsuWbB1fZQoaAZoCWgPQwifzarP1dbhv5SGlFKUaBVLMmgWR0C8vPunhsIndX2UKGgGaAloD0MInu3RG+4j37+UhpRSlGgVSzJoFkdAvLzdcxCY1HV9lChoBmgJaA9DCPSpY5XSM9m/lIaUUpRoFUsyaBZHQLy8uMMI/qx1fZQoaAZoCWgPQwjWxW00gLfav5SGlFKUaBVLMmgWR0C8vp/fGdZrdX2UKGgGaAloD0MIc9anHJPF5L+UhpRSlGgVSzJoFkdAvL6CY0EX+HV9lChoBmgJaA9DCDSFzmvskuC/lIaUUpRoFUsyaBZHQLy+ZDYRNAV1fZQoaAZoCWgPQwjDnQsjvajTv5SGlFKUaBVLMmgWR0C8vj+TA31jdX2UKGgGaAloD0MIv7hUpS2u3L+UhpRSlGgVSzJoFkdAvMAZtcfNinV9lChoBmgJaA9DCH2UEReARta/lIaUUpRoFUsyaBZHQLy//EAo5Px1fZQoaAZoCWgPQwjU0XE1sivbv5SGlFKUaBVLMmgWR0C8v936AOJ+dX2UKGgGaAloD0MIy4CzlCwn3L+UhpRSlGgVSzJoFkdAvL+5RFZxJnV9lChoBmgJaA9DCL6/QXv18eC/lIaUUpRoFUsyaBZHQLzBjJHAh0R1fZQoaAZoCWgPQwhP6WD9n8Pbv5SGlFKUaBVLMmgWR0C8wW8Y64lQdX2UKGgGaAloD0MInWSryykB27+UhpRSlGgVSzJoFkdAvMFQyAQQMHV9lChoBmgJaA9DCMOf4c0avNO/lIaUUpRoFUsyaBZHQLzBLBomG/N1fZQoaAZoCWgPQwjEP2zp0VTgv5SGlFKUaBVLMmgWR0C8wv2ykbgkdX2UKGgGaAloD0MIn47HDFTG17+UhpRSlGgVSzJoFkdAvMLgNUfgaXV9lChoBmgJaA9DCE7RkVz+Q9W/lIaUUpRoFUsyaBZHQLzCwgNwzch1fZQoaAZoCWgPQwiQa0PFOP/kv5SGlFKUaBVLMmgWR0C8wp1LnLaFdX2UKGgGaAloD0MIE0VI3c4+4L+UhpRSlGgVSzJoFkdAvMRxe8f3e3V9lChoBmgJaA9DCK6f/rPmx9q/lIaUUpRoFUsyaBZHQLzEVAaef7J1fZQoaAZoCWgPQwiW58HdWbvev5SGlFKUaBVLMmgWR0C8xDXJDE3sdX2UKGgGaAloD0MI8n1xqUrb5L+UhpRSlGgVSzJoFkdAvMQRIK+i8HV9lChoBmgJaA9DCP+Xa9ECtN+/lIaUUpRoFUsyaBZHQLzF5fJ3gUF1fZQoaAZoCWgPQwhrC89LxcbUv5SGlFKUaBVLMmgWR0C8xciDujREdX2UKGgGaAloD0MIMNgN2xZl07+UhpRSlGgVSzJoFkdAvMWqO5rgwXV9lChoBmgJaA9DCEwW9x+ZjuC/lIaUUpRoFUsyaBZHQLzFhYXO4Xp1fZQoaAZoCWgPQwj+DkWBPpHfv5SGlFKUaBVLMmgWR0C8x1fQKKHgdX2UKGgGaAloD0MI+1ksRfKV2b+UhpRSlGgVSzJoFkdAvMc6W4Vh1HV9lChoBmgJaA9DCOiiIeNRKtq/lIaUUpRoFUsyaBZHQLzHHB8hLXd1fZQoaAZoCWgPQwgyPPazWAriv5SGlFKUaBVLMmgWR0C8xvdyPuG9dX2UKGgGaAloD0MI8pVASuza1b+UhpRSlGgVSzJoFkdAvMjIQ4CIUXV9lChoBmgJaA9DCCmvldBdEt6/lIaUUpRoFUsyaBZHQLzIqs052hZ1fZQoaAZoCWgPQwjbiv1l9+Tbv5SGlFKUaBVLMmgWR0C8yIyOJcgRdX2UKGgGaAloD0MIaw4QzNHj0r+UhpRSlGgVSzJoFkdAvMhn1wo9cXV9lChoBmgJaA9DCEELCRhdXuK/lIaUUpRoFUsyaBZHQLzKWOUt7KJ1fZQoaAZoCWgPQwgN/+kGCrzgv5SGlFKUaBVLMmgWR0C8yjtwvQF+dX2UKGgGaAloD0MIUwPN59zt3b+UhpRSlGgVSzJoFkdAvModObiIcnV9lChoBmgJaA9DCKN3KuCe5+O/lIaUUpRoFUsyaBZHQLzJ+Nzr/sF1fZQoaAZoCWgPQwiDUN7H0Zzkv5SGlFKUaBVLMmgWR0C8y+Dy4FzNdX2UKGgGaAloD0MIMo6R7BFq5L+UhpRSlGgVSzJoFkdAvMvDmJWNm3V9lChoBmgJaA9DCKlorP2d7da/lIaUUpRoFUsyaBZHQLzLpXKr7wd1fZQoaAZoCWgPQwjbbRea6zTdv5SGlFKUaBVLMmgWR0C8y4DUAks0dX2UKGgGaAloD0MI4/viUpW23L+UhpRSlGgVSzJoFkdAvM1gasIVunV9lChoBmgJaA9DCE+vlGWIY9i/lIaUUpRoFUsyaBZHQLzNQvoePq91fZQoaAZoCWgPQwgexM4UOq/dv5SGlFKUaBVLMmgWR0C8zSS/fwZwdX2UKGgGaAloD0MIyR8MPPce1b+UhpRSlGgVSzJoFkdAvM0ACCBf8nV9lChoBmgJaA9DCK/pQUEp2uC/lIaUUpRoFUsyaBZHQLzO0XuVopR1fZQoaAZoCWgPQwgRxHk4geniv5SGlFKUaBVLMmgWR0C8zrQG4ZuRdX2UKGgGaAloD0MIRBmqYir91b+UhpRSlGgVSzJoFkdAvM6VuuRs/XV9lChoBmgJaA9DCOOJIM7DCdK/lIaUUpRoFUsyaBZHQLzOcP557gN1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "_n_updates": 249975, "buffer_size": 1000000, "batch_size": 2048, "learning_starts": 100, "tau": 0.05, "gamma": 0.95, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function DictReplayBuffer.__init__ at 0x7fb6622c1040>", "add": "<function DictReplayBuffer.add at 0x7fb6622c10d0>", "sample": "<function DictReplayBuffer.sample at 0x7fb6622c1160>", "_get_samples": "<function DictReplayBuffer._get_samples at 0x7fb6622c11f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb6622c0330>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -3.0, "ent_coef": "auto", "target_update_interval": 1, "top_quantiles_to_drop_per_net": 2, "batch_norm_stats": [], "batch_norm_stats_target": [], "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": -0.202277074570884, "std_reward": 0.10541229020290471, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-22T16:17:37.002249"}

tqc-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:477375b177134472ecebf7f800bdc4ab7b7282517ba129532d5def638ce775ad
+size 3346028

tqc-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

tqc-PandaReachDense-v2/actor.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4f51156d2abbe96461e1117b2d8dfad731ee11000acc7f3581d0e07f9a63b03
+size 571805

tqc-PandaReachDense-v2/critic.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6b869061eaa1a18f3346d5987722b70caaa2a12642329d6b4035e06af30f321
+size 1230585

tqc-PandaReachDense-v2/data

@@ -0,0 +1,111 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
+        "__module__": "sb3_contrib.tqc.policies",
+        "__doc__": "\n    Policy class (with both actor and critic) for TQC.\n\n    :param observation_space: Observation space\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\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    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
+        "__init__": "<function MultiInputPolicy.__init__ at 0x7fb60b7c3c10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb60b7bfc30>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        "use_sde": 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": "RandomState(MT19937)"
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674396849105999628,
+    "learning_rate": 0.001,
+    "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": "[[0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]\n [0.43644726 0.00273863 0.52870303]]",
+        "desired_goal": "[[ 0.76670194 -0.356108    0.685178  ]\n [ 0.6586274  -0.8357658   0.42670015]\n [-0.71320754 -0.85692686 -0.4155283 ]\n [ 1.7247589   1.0068423  -1.0498981 ]]",
+        "observation": "[[ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]\n [ 4.3644726e-01  2.7386297e-03  5.2870303e-01  7.7044472e-02\n  -7.8114339e-05 -2.1706222e-02]]"
+    },
+    "_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": "[[ 0.06641828 -0.03104412  0.21035875]\n [ 0.05702582 -0.07258389  0.18791056]\n [-0.0621965  -0.0744165   0.11476498]\n [ 0.1496803   0.08699138  0.05967144]]",
+        "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": 20000,
+    "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:": "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"
+    },
+    "_n_updates": 249975,
+    "buffer_size": 1000000,
+    "batch_size": 2048,
+    "learning_starts": 100,
+    "tau": 0.05,
+    "gamma": 0.95,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=",
+        "__module__": "stable_baselines3.common.buffers",
+        "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function DictReplayBuffer.__init__ at 0x7fb6622c1040>",
+        "add": "<function DictReplayBuffer.add at 0x7fb6622c10d0>",
+        "sample": "<function DictReplayBuffer.sample at 0x7fb6622c1160>",
+        "_get_samples": "<function DictReplayBuffer._get_samples at 0x7fb6622c11f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb6622c0330>"
+    },
+    "replay_buffer_kwargs": {},
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -3.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "top_quantiles_to_drop_per_net": 2,
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
+}

tqc-PandaReachDense-v2/ent_coef_optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7064e285d28ecfd4b82f87c2b0f616197faa40b8edf0c945c133f48996168a07
+size 1507

tqc-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c44125ec579e2d562a893bf991457540804a7f54141f6a1909321a4f2dd58a9f
+size 1515205

tqc-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a0d62f0dde47465d8629f6c11361bf4f8461afaedf127143dc570b927b667be
+size 747

tqc-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

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e9aa213c5b4496ba0a2c34e7589b2e4ff781ac12c9e0b5e508276577e846ce6
+size 3056