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: -0.71 +/- 0.26
+      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:e06778d608c31488e37763096009625f91c5eb335f60a46ede7abf87fc7464a7
+size 109565

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/data

@@ -0,0 +1,97 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f1a54039f70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f1a54037fc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 400000,
+    "_total_timesteps": 400000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681844207113003370,
+    "learning_rate": 0.00096,
+    "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": "[[ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]]",
+        "desired_goal": "[[-0.94046485 -0.32338834  0.17902899]\n [-0.9868354  -0.5311005   1.3613518 ]\n [-1.487946    0.75155646 -0.3274547 ]\n [ 1.3380976  -1.5186019   0.49701482]]",
+        "observation": "[[ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]]"
+    },
+    "_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": "[[-0.0817953   0.04305157  0.17564419]\n [-0.03561927  0.12513036  0.14027993]\n [-0.05790976 -0.05587965  0.07765549]\n [-0.01138334 -0.04985952  0.27319872]]",
+        "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": true,
+    "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": 12500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "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 sha256:5b7faa93f3061cdd84d5dd8c28fd75a0ea95abc7ce92715a29902a8c2376ee18
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:134ac3e000e0b089204f5bea2de8d3ecdec56cfa466a9dd84b280d4970921ff5
+size 46718

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 0x7f1a54039f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f1a54037fc0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 400000, "_total_timesteps": 400000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681844207113003370, "learning_rate": 0.00096, "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": "[[ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]\n [ 0.01424363 -0.07413551  0.7732198 ]]", "desired_goal": "[[-0.94046485 -0.32338834  0.17902899]\n [-0.9868354  -0.5311005   1.3613518 ]\n [-1.487946    0.75155646 -0.3274547 ]\n [ 1.3380976  -1.5186019   0.49701482]]", "observation": "[[ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]\n [ 0.01424363 -0.07413551  0.7732198   0.04168929  0.00095009  0.07290921]]"}, "_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": "[[-0.0817953   0.04305157  0.17564419]\n [-0.03561927  0.12513036  0.14027993]\n [-0.05790976 -0.05587965  0.07765549]\n [-0.01138334 -0.04985952  0.27319872]]", "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": true, "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": 12500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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": -0.7118706171633675, "std_reward": 0.2635581606750517, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-18T19:17:55.209783"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42ade98297090fcc4d20ad0dfbe7366ef39e44641f3a07f15754a57bf0030783
+size 2381