YoavWigelman commited on
Commit
f301fb2
1 Parent(s): c755997

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: -0.26 +/- 0.13
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:e2c3a97bac1de8779960c2dae50655d34435d5487a15d37ab55d7824a36c0d0e
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 0x79ea5d54a5f0>",
8
+ "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x79ea5d53f7c0>"
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": 1692711008691893142,
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": "[[ 1.0610498 0.44135362 0.24309702]\n [-0.62427557 -0.09612593 0.30738282]\n [-0.58640057 0.41895422 0.3211758 ]\n [-0.9456762 0.4513962 -1.346609 ]]",
34
+ "desired_goal": "[[ 1.3827622 1.0746428 -0.6416805 ]\n [-1.3749186 -0.1465978 1.4908316 ]\n [-0.38815308 0.7190282 1.5670913 ]\n [-1.176422 0.402662 -1.2881026 ]]",
35
+ "observation": "[[ 1.0610498 0.44135362 0.24309702 1.5914797 1.6887981 -1.1554008 ]\n [-0.62427557 -0.09612593 0.30738282 -0.9402154 -0.05972236 0.8266304 ]\n [-0.58640057 0.41895422 0.3211758 -0.7653771 1.7014413 0.873304 ]\n [-0.9456762 0.4513962 -1.346609 -1.0110993 -0.02955962 -0.9465465 ]]"
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": "[[-0.10067444 -0.05704798 0.19977438]\n [ 0.12003545 -0.09100444 0.2867015 ]\n [ 0.0577695 0.07272559 0.21417119]\n [-0.0396908 0.04689586 0.23441581]]",
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 sha256:3c8cdd17ca8eff2c2daa6ba9d6501371026dcec312c836722fd89245d9fd3765
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:ecbcc6917741054633d32b5314268ea85840beef8ca103693b780ddb653ee58b
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 0x79ea5d54a5f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79ea5d53f7c0>"}, "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": 1692711008691893142, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 1.0610498 0.44135362 0.24309702]\n [-0.62427557 -0.09612593 0.30738282]\n [-0.58640057 0.41895422 0.3211758 ]\n [-0.9456762 0.4513962 -1.346609 ]]", "desired_goal": "[[ 1.3827622 1.0746428 -0.6416805 ]\n [-1.3749186 -0.1465978 1.4908316 ]\n [-0.38815308 0.7190282 1.5670913 ]\n [-1.176422 0.402662 -1.2881026 ]]", "observation": "[[ 1.0610498 0.44135362 0.24309702 1.5914797 1.6887981 -1.1554008 ]\n [-0.62427557 -0.09612593 0.30738282 -0.9402154 -0.05972236 0.8266304 ]\n [-0.58640057 0.41895422 0.3211758 -0.7653771 1.7014413 0.873304 ]\n [-0.9456762 0.4513962 -1.346609 -1.0110993 -0.02955962 -0.9465465 ]]"}, "_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": "[[-0.10067444 -0.05704798 0.19977438]\n [ 0.12003545 -0.09100444 0.2867015 ]\n [ 0.0577695 0.07272559 0.21417119]\n [-0.0396908 0.04689586 0.23441581]]", "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 (673 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -0.25559861892834307, "std_reward": 0.12794827850617924, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-22T14:25:18.681376"}
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fdabe38a3891ef8817113f14065789437fe404259d5898555b33d1518f192969
3
+ size 2623