jcramirezpr commited on
Commit
00f48b2
1 Parent(s): 8b039bd

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: -7.74 +/- 2.91
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:e8dd6a82896eeeb69d29ef9e6724d4b0c72e5e9838e27392383c42184e2aa1a0
3
+ size 108028
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 0x7f569beb8790>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7f569beb6c00>"
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": 1679275244361134144,
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": "[[ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]]",
60
+ "desired_goal": "[[ 1.5539047 0.12678665 0.5347511 ]\n [-1.1468259 -0.96542 0.20894451]\n [-0.27297524 -1.5465124 0.6239385 ]\n [-1.5394646 -0.5607963 1.362263 ]]",
61
+ "observation": "[[ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]]"
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": "[[-0.03754401 0.08721258 0.00835037]\n [-0.14823954 -0.01001636 0.08769138]\n [-0.06331612 0.1488317 0.23619919]\n [ 0.01923868 -0.00952862 0.02002423]]",
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 sha256:80ed981e72bc9f93a73c7ddb25848aea20af6e6e6d50894ccee35fcbe48194a9
3
+ size 44734
a2c-PandaReachDense-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:21692a90a588fdc7dd2995926e1767c5e0a481f8dbe3d58846af6d1aefb61c9e
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
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 0x7f569beb8790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f569beb6c00>"}, "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": 1679275244361134144, "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": "[[ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]\n [ 0.38724354 -0.08337657 0.7266827 ]]", "desired_goal": "[[ 1.5539047 0.12678665 0.5347511 ]\n [-1.1468259 -0.96542 0.20894451]\n [-0.27297524 -1.5465124 0.6239385 ]\n [-1.5394646 -0.5607963 1.362263 ]]", "observation": "[[ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]\n [ 0.38724354 -0.08337657 0.7266827 -0.00332351 -0.00965323 0.02231379]]"}, "_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": "[[-0.03754401 0.08721258 0.00835037]\n [-0.14823954 -0.01001636 0.08769138]\n [-0.06331612 0.1488317 0.23619919]\n [ 0.01923868 -0.00952862 0.02002423]]", "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (885 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -7.737307943589985, "std_reward": 2.9104061484233084, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-20T02:46:27.410131"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:78b7d73e22538a7ee27f2c4826a5561a3d5eaf89f537bf22b2e0c44e14bb5cd9
3
+ size 3056