msgerasyov commited on
Commit
c6cfb2a
·
1 Parent(s): 7c25b35

Initial commit

Browse files
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - PandaReachDense-v2
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-v2
16
+ type: PandaReachDense-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: -10.49 +/- 3.94
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **A2C** Agent playing **PandaReachDense-v2**
25
+ This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
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-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b4c98de9e78e6b195f2536257a46fe778b49d336e31b130ee218f986553457b2
3
+ size 108023
a2c-PandaReachDense-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
a2c-PandaReachDense-v2/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7f33bc4a6430>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc_data object at 0x7f33bc4a81e0>"
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
+ "observation_space": {
23
+ ":type:": "<class 'gym.spaces.dict.Dict'>",
24
+ ":serialized:": "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",
25
+ "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))])",
26
+ "_shape": null,
27
+ "dtype": null,
28
+ "_np_random": null
29
+ },
30
+ "action_space": {
31
+ ":type:": "<class 'gym.spaces.box.Box'>",
32
+ ":serialized:": "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",
33
+ "dtype": "float32",
34
+ "_shape": [
35
+ 3
36
+ ],
37
+ "low": "[-1. -1. -1.]",
38
+ "high": "[1. 1. 1.]",
39
+ "bounded_below": "[ True True True]",
40
+ "bounded_above": "[ True True True]",
41
+ "_np_random": null
42
+ },
43
+ "n_envs": 4,
44
+ "num_timesteps": 1000000,
45
+ "_total_timesteps": 1000000,
46
+ "_num_timesteps_at_start": 0,
47
+ "seed": null,
48
+ "action_noise": null,
49
+ "start_time": 1674026345574669174,
50
+ "learning_rate": 0.0007,
51
+ "tensorboard_log": null,
52
+ "lr_schedule": {
53
+ ":type:": "<class 'function'>",
54
+ ":serialized:": "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"
55
+ },
56
+ "_last_obs": {
57
+ ":type:": "<class 'collections.OrderedDict'>",
58
+ ":serialized:": "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",
59
+ "achieved_goal": "[[-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]]",
60
+ "desired_goal": "[[-1.639339 -1.4489026 1.1495146 ]\n [ 0.29918078 -0.978559 0.48925936]\n [-0.02412884 1.1524426 -0.16744371]\n [ 0.8539172 1.6200674 -0.11166795]]",
61
+ "observation": "[[-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]]"
62
+ },
63
+ "_last_episode_starts": {
64
+ ":type:": "<class 'numpy.ndarray'>",
65
+ ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
66
+ },
67
+ "_last_original_obs": {
68
+ ":type:": "<class 'collections.OrderedDict'>",
69
+ ":serialized:": "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",
70
+ "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]]",
71
+ "desired_goal": "[[-0.12146655 -0.05094685 0.07145475]\n [ 0.03274106 0.13384198 0.1707962 ]\n [ 0.10669979 0.09755883 0.2970707 ]\n [-0.12148206 0.13942346 0.09863423]]",
72
+ "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]]"
73
+ },
74
+ "_episode_num": 0,
75
+ "use_sde": false,
76
+ "sde_sample_freq": -1,
77
+ "_current_progress_remaining": 0.0,
78
+ "ep_info_buffer": {
79
+ ":type:": "<class 'collections.deque'>",
80
+ ":serialized:": "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"
81
+ },
82
+ "ep_success_buffer": {
83
+ ":type:": "<class 'collections.deque'>",
84
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
85
+ },
86
+ "_n_updates": 50000,
87
+ "n_steps": 5,
88
+ "gamma": 0.99,
89
+ "gae_lambda": 1.0,
90
+ "ent_coef": 0.0,
91
+ "vf_coef": 0.5,
92
+ "max_grad_norm": 0.5,
93
+ "normalize_advantage": false
94
+ }
a2c-PandaReachDense-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bef91dd162687ad4efc5c8dbaf1f0764fb744ef4847fc57dd066d79412f85297
3
+ size 44734
a2c-PandaReachDense-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2d45ba605d36a14284794dcb7b3b6ac87db419a9c45167a60bb22f279ff4b460
3
+ size 46014
a2c-PandaReachDense-v2/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-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.10
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.21.6
7
+ - Gym: 0.21.0
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 0x7f33bc4a6430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f33bc4a81e0>"}, "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": 1674026345574669174, "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": "[[-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]\n [-0.19614832 -0.0178323 0.2621736 ]]", "desired_goal": "[[-1.639339 -1.4489026 1.1495146 ]\n [ 0.29918078 -0.978559 0.48925936]\n [-0.02412884 1.1524426 -0.16744371]\n [ 0.8539172 1.6200674 -0.11166795]]", "observation": "[[-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]\n [-0.19614832 -0.0178323 0.2621736 -0.02126489 0.00094522 0.02206409]]"}, "_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": "[[-0.12146655 -0.05094685 0.07145475]\n [ 0.03274106 0.13384198 0.1707962 ]\n [ 0.10669979 0.09755883 0.2970707 ]\n [-0.12148206 0.13942346 0.09863423]]", "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"}}
replay.mp4 ADDED
Binary file (739 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -10.488651083037258, "std_reward": 3.9443328900522854, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T08:03:12.367571"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2e852d559d6f725ffaf0902bb664614d86ba5c94ba5a8cd250d05c470c141e91
3
+ size 3212