pabloyesteb commited on
Commit
c587c9d
1 Parent(s): 326e866

Initial commit

Browse files
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - PandaReachDense-v3
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: A2C
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: PandaReachDense-v3
16
+ type: PandaReachDense-v3
17
+ metrics:
18
+ - type: mean_reward
19
+ value: -0.21 +/- 0.10
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **A2C** Agent playing **PandaReachDense-v3**
25
+ This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
a2c-PandaReachDense-v3.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:222521e746ae748c215437c2a7aadb5b6999f3aa9d694b1dd7ca1f73b9911a13
3
+ size 106831
a2c-PandaReachDense-v3/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 2.1.0
a2c-PandaReachDense-v3/data ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__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 ",
7
+ "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7d151c470940>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7d151c45ab80>"
10
+ },
11
+ "verbose": 1,
12
+ "policy_kwargs": {
13
+ ":type:": "<class 'dict'>",
14
+ ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
15
+ "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
16
+ "optimizer_kwargs": {
17
+ "alpha": 0.99,
18
+ "eps": 1e-05,
19
+ "weight_decay": 0
20
+ }
21
+ },
22
+ "num_timesteps": 1000000,
23
+ "_total_timesteps": 1000000,
24
+ "_num_timesteps_at_start": 0,
25
+ "seed": null,
26
+ "action_noise": null,
27
+ "start_time": 1692833177819425388,
28
+ "learning_rate": 0.0007,
29
+ "tensorboard_log": null,
30
+ "_last_obs": {
31
+ ":type:": "<class 'collections.OrderedDict'>",
32
+ ":serialized:": "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",
33
+ "achieved_goal": "[[ 1.0126688 0.4551586 0.25842154]\n [ 1.0126688 0.4551586 0.25842154]\n [-0.11511973 -0.44870403 -0.1887511 ]\n [ 0.05540269 -0.44010806 -0.18430562]]",
34
+ "desired_goal": "[[ 1.5026294 1.2169497 -0.84678626]\n [ 1.1428696 0.6162475 0.09877397]\n [-0.7630503 -0.5205501 -0.22825003]\n [ 0.50272655 -1.5322115 -0.38945925]]",
35
+ "observation": "[[ 1.0126688 0.4551586 0.25842154 1.584043 1.6894325 -1.1634977 ]\n [ 1.0126688 0.4551586 0.25842154 1.584043 1.6894325 -1.1634977 ]\n [-0.11511973 -0.44870403 -0.1887511 -1.802414 -1.7393725 -1.3783208 ]\n [ 0.05540269 -0.44010806 -0.18430562 -0.6640645 -1.7162651 -1.2847391 ]]"
36
+ },
37
+ "_last_episode_starts": {
38
+ ":type:": "<class 'numpy.ndarray'>",
39
+ ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
40
+ },
41
+ "_last_original_obs": {
42
+ ":type:": "<class 'collections.OrderedDict'>",
43
+ ":serialized:": "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",
44
+ "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]]",
45
+ "desired_goal": "[[ 0.12372662 -0.1447478 0.12511373]\n [ 0.04765599 -0.10503709 0.00026812]\n [-0.09659427 0.1177965 0.14847724]\n [ 0.0526129 -0.05354679 0.12651914]]",
46
+ "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]]"
47
+ },
48
+ "_episode_num": 0,
49
+ "use_sde": false,
50
+ "sde_sample_freq": -1,
51
+ "_current_progress_remaining": 0.0,
52
+ "_stats_window_size": 100,
53
+ "ep_info_buffer": {
54
+ ":type:": "<class 'collections.deque'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "ep_success_buffer": {
58
+ ":type:": "<class 'collections.deque'>",
59
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
60
+ },
61
+ "_n_updates": 50000,
62
+ "n_steps": 5,
63
+ "gamma": 0.99,
64
+ "gae_lambda": 1.0,
65
+ "ent_coef": 0.0,
66
+ "vf_coef": 0.5,
67
+ "max_grad_norm": 0.5,
68
+ "normalize_advantage": false,
69
+ "observation_space": {
70
+ ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
71
+ ":serialized:": "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",
72
+ "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
73
+ "_shape": null,
74
+ "dtype": null,
75
+ "_np_random": null
76
+ },
77
+ "action_space": {
78
+ ":type:": "<class 'gymnasium.spaces.box.Box'>",
79
+ ":serialized:": "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",
80
+ "dtype": "float32",
81
+ "bounded_below": "[ True True True]",
82
+ "bounded_above": "[ True True True]",
83
+ "_shape": [
84
+ 3
85
+ ],
86
+ "low": "[-1. -1. -1.]",
87
+ "high": "[1. 1. 1.]",
88
+ "low_repr": "-1.0",
89
+ "high_repr": "1.0",
90
+ "_np_random": null
91
+ },
92
+ "n_envs": 4,
93
+ "lr_schedule": {
94
+ ":type:": "<class 'function'>",
95
+ ":serialized:": "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"
96
+ }
97
+ }
a2c-PandaReachDense-v3/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8e183ef0484ac0105d3e449be547396b29202ae554d1cf3eaf6b91abc6daa8d1
3
+ size 44734
a2c-PandaReachDense-v3/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f30b127d10ae1313f26c84338503c8109ce6223678978f238b72de71fef9f3f1
3
+ size 46014
a2c-PandaReachDense-v3/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
a2c-PandaReachDense-v3/system_info.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
2
+ - Python: 3.10.12
3
+ - Stable-Baselines3: 2.1.0
4
+ - PyTorch: 2.0.1+cu118
5
+ - GPU Enabled: True
6
+ - Numpy: 1.23.5
7
+ - Cloudpickle: 2.2.1
8
+ - Gymnasium: 0.29.1
9
+ - OpenAI Gym: 0.25.2
config.json ADDED
@@ -0,0 +1 @@
 
 
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 0x7d151c470940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d151c45ab80>"}, "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": 1692833177819425388, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 1.0126688 0.4551586 0.25842154]\n [ 1.0126688 0.4551586 0.25842154]\n [-0.11511973 -0.44870403 -0.1887511 ]\n [ 0.05540269 -0.44010806 -0.18430562]]", "desired_goal": "[[ 1.5026294 1.2169497 -0.84678626]\n [ 1.1428696 0.6162475 0.09877397]\n [-0.7630503 -0.5205501 -0.22825003]\n [ 0.50272655 -1.5322115 -0.38945925]]", "observation": "[[ 1.0126688 0.4551586 0.25842154 1.584043 1.6894325 -1.1634977 ]\n [ 1.0126688 0.4551586 0.25842154 1.584043 1.6894325 -1.1634977 ]\n [-0.11511973 -0.44870403 -0.1887511 -1.802414 -1.7393725 -1.3783208 ]\n [ 0.05540269 -0.44010806 -0.18430562 -0.6640645 -1.7162651 -1.2847391 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.12372662 -0.1447478 0.12511373]\n [ 0.04765599 -0.10503709 0.00026812]\n [-0.09659427 0.1177965 0.14847724]\n [ 0.0526129 -0.05354679 0.12651914]]", "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}
replay.mp4 ADDED
Binary file (663 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -0.2093866671901196, "std_reward": 0.09753889968337125, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-24T00:15:08.495127"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dfae1ce29088597d49fc0512e43c15227b489262b93c073d2e3f46573fb74f93
3
+ size 2623