DiegoD616 commited on
Commit
94fc790
·
1 Parent(s): 1f6a556

New model for PandaReachDense-v2

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: -2.42 +/- 0.49
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:dbddad97b58ed0213142982e0146a5502ae14ce8d8ee3da45d082d34475a00f3
3
+ size 107852
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 0x7f6a44e5cca0>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc_data object at 0x7f6a44e6db40>"
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": 1674420837228887814,
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": "[[ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]]",
60
+ "desired_goal": "[[-0.55212146 0.89755154 -1.4958884 ]\n [ 0.6227911 -0.75624466 0.20514457]\n [ 0.73307306 0.790775 0.72482693]\n [-0.2253413 -1.6723441 -1.1424091 ]]",
61
+ "observation": "[[ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]]"
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": "[[-0.0132405 0.10637856 0.05561024]\n [-0.06895059 -0.04658497 0.21453384]\n [ 0.11348439 -0.1296086 0.0466111 ]\n [-0.04464121 0.07178638 0.08048032]]",
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:c5c514ceb47ddf14ef0c7534807518d69d741a73a53a3165b497a8b352691ab5
3
+ size 44606
a2c-PandaReachDense-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5dbdd9b34d510bb00d43b4fcf35f29cf610049679542b672405b6f72436a48ed
3
+ size 45886
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: False
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 0x7f6a44e5cca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6a44e6db40>"}, "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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": 1674420837228887814, "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": "[[ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]\n [ 0.44364563 -0.0568875 0.5801696 ]]", "desired_goal": "[[-0.55212146 0.89755154 -1.4958884 ]\n [ 0.6227911 -0.75624466 0.20514457]\n [ 0.73307306 0.790775 0.72482693]\n [-0.2253413 -1.6723441 -1.1424091 ]]", "observation": "[[ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]\n [ 4.4364563e-01 -5.6887496e-02 5.8016962e-01 1.4232207e-04\n -4.7654412e-03 5.7977815e-03]]"}, "_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": "[[-0.0132405 0.10637856 0.05561024]\n [-0.06895059 -0.04658497 0.21453384]\n [ 0.11348439 -0.1296086 0.0466111 ]\n [-0.04464121 0.07178638 0.08048032]]", "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": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}
replay.mp4 ADDED
Binary file (701 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -2.416536821331829, "std_reward": 0.49283529651864444, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-22T21:43:47.197735"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5ada1d38c930ae3fd11d248b5b8ee6f9a968375efceb166311abbb1846978799
3
+ size 3049