Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +13 -13
- a2c-PandaReachDense-v2/policy.optimizer.pth +2 -2
- a2c-PandaReachDense-v2/policy.pth +2 -2
- a2c-PandaReachDense-v2/system_info.txt +5 -5
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +2 -2
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: PandaReachDense-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value: -
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: PandaReachDense-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: -1.87 +/- 0.76
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
a2c-PandaReachDense-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2be94c155095f086a8c5cec8329e15c61aebe28b564926f526e60cc071117350
|
3 |
+
size 107808
|
a2c-PandaReachDense-v2/data
CHANGED
@@ -4,9 +4,9 @@
|
|
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
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
-
"_abc_impl": "<_abc_data object at
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
@@ -24,19 +24,19 @@
|
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
-
"start_time":
|
28 |
"learning_rate": 0.0007,
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
31 |
":type:": "<class 'function'>",
|
32 |
-
":serialized:": "
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
-
":serialized:": "
|
37 |
-
"achieved_goal": "[[
|
38 |
-
"desired_goal": "[[-
|
39 |
-
"observation": "[[ 0.
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -44,9 +44,9 @@
|
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
-
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
48 |
"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]]",
|
49 |
-
"desired_goal": "[[
|
50 |
"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]]"
|
51 |
},
|
52 |
"_episode_num": 0,
|
@@ -56,7 +56,7 @@
|
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
@@ -72,7 +72,7 @@
|
|
72 |
"normalize_advantage": false,
|
73 |
"observation_space": {
|
74 |
":type:": "<class 'gym.spaces.dict.Dict'>",
|
75 |
-
":serialized:": "
|
76 |
"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))])",
|
77 |
"_shape": null,
|
78 |
"dtype": null,
|
@@ -80,7 +80,7 @@
|
|
80 |
},
|
81 |
"action_space": {
|
82 |
":type:": "<class 'gym.spaces.box.Box'>",
|
83 |
-
":serialized:": "
|
84 |
"dtype": "float32",
|
85 |
"_shape": [
|
86 |
3
|
|
|
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 0x7f3b15acbf40>",
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f3b15ad8040>"
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
|
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
+
"start_time": 1684595237462983758,
|
28 |
"learning_rate": 0.0007,
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
31 |
":type:": "<class 'function'>",
|
32 |
+
":serialized:": "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"
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
+
":serialized:": "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",
|
37 |
+
"achieved_goal": "[[0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]]",
|
38 |
+
"desired_goal": "[[-0.53663754 0.6035303 0.9390433 ]\n [ 0.93018675 -0.74466515 0.32324192]\n [-0.3144939 -0.23137347 -1.248634 ]\n [-1.2539475 1.2227048 0.02954706]]",
|
39 |
+
"observation": "[[ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]]"
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
+
":serialized:": "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",
|
48 |
"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]]",
|
49 |
+
"desired_goal": "[[-0.1412461 -0.01334507 0.0321696 ]\n [ 0.0617462 0.00994262 0.29383585]\n [ 0.04036948 0.09702305 0.11217347]\n [-0.0124154 0.14044027 0.10774479]]",
|
50 |
"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]]"
|
51 |
},
|
52 |
"_episode_num": 0,
|
|
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
+
":serialized:": "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"
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
|
|
72 |
"normalize_advantage": false,
|
73 |
"observation_space": {
|
74 |
":type:": "<class 'gym.spaces.dict.Dict'>",
|
75 |
+
":serialized:": "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",
|
76 |
"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))])",
|
77 |
"_shape": null,
|
78 |
"dtype": null,
|
|
|
80 |
},
|
81 |
"action_space": {
|
82 |
":type:": "<class 'gym.spaces.box.Box'>",
|
83 |
+
":serialized:": "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",
|
84 |
"dtype": "float32",
|
85 |
"_shape": [
|
86 |
3
|
a2c-PandaReachDense-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d55bbc5706df437fa6a918426f606850ac61bc93c8944009eb0978622e2eb928
|
3 |
+
size 44606
|
a2c-PandaReachDense-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c206590701aa0a543a83f95ddd421e3b0111119cf20f774ea18db79ecc1853d2
|
3 |
+
size 45886
|
a2c-PandaReachDense-v2/system_info.txt
CHANGED
@@ -1,7 +1,7 @@
|
|
1 |
-
- OS: Linux-5.15.
|
2 |
-
- Python: 3.
|
3 |
- Stable-Baselines3: 1.8.0
|
4 |
-
- PyTorch: 2.0.1+
|
5 |
-
- GPU Enabled:
|
6 |
-
- Numpy: 1.
|
7 |
- Gym: 0.21.0
|
|
|
1 |
+
- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
|
2 |
+
- Python: 3.10.11
|
3 |
- Stable-Baselines3: 1.8.0
|
4 |
+
- PyTorch: 2.0.1+cu118
|
5 |
+
- GPU Enabled: False
|
6 |
+
- Numpy: 1.22.4
|
7 |
- Gym: 0.21.0
|
config.json
CHANGED
@@ -1 +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 0x7fe659f2d160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe659f26ab0>"}, "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": 1684595340929020899, "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": "[[ 0.35739404 -0.00491314 0.61196893]\n [ 0.35739404 -0.00491314 0.61196893]\n [ 0.35739404 -0.00491314 0.61196893]\n [ 0.35739404 -0.00491314 0.61196893]]", "desired_goal": "[[-1.0473456 0.437192 -0.5400255 ]\n [-1.188204 -0.05925938 0.76055837]\n [-1.3686596 0.48178503 0.43305603]\n [-0.18455364 -1.347674 -1.5640165 ]]", "observation": "[[ 0.35739404 -0.00491314 0.61196893 0.00515332 0.00353574 0.0045442 ]\n [ 0.35739404 -0.00491314 0.61196893 0.00515332 0.00353574 0.0045442 ]\n [ 0.35739404 -0.00491314 0.61196893 0.00515332 0.00353574 0.0045442 ]\n [ 0.35739404 -0.00491314 0.61196893 0.00515332 0.00353574 0.0045442 ]]"}, "_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": "[[ 0.02488541 0.03823375 0.03426935]\n [ 0.11647554 0.1324544 0.07247093]\n [ 0.03896362 -0.12771627 0.10189251]\n [ 0.12996902 -0.0092485 0.09220858]]", "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 '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, "system_info": {"OS": "Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.17 # 1 SMP Fri Jan 27 02:56:13 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.3", "Gym": "0.21.0"}}
|
|
|
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 0x7f3b15acbf40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3b15ad8040>"}, "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": 1684595237462983758, "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": "[[0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]\n [0.4466806 0.01054829 0.58460623]]", "desired_goal": "[[-0.53663754 0.6035303 0.9390433 ]\n [ 0.93018675 -0.74466515 0.32324192]\n [-0.3144939 -0.23137347 -1.248634 ]\n [-1.2539475 1.2227048 0.02954706]]", "observation": "[[ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]\n [ 0.4466806 0.01054829 0.58460623 0.00855346 -0.00082908 0.01274963]]"}, "_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": "[[-0.1412461 -0.01334507 0.0321696 ]\n [ 0.0617462 0.00994262 0.29383585]\n [ 0.04036948 0.09702305 0.11217347]\n [-0.0124154 0.14044027 0.10774479]]", "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 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVWAMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZSMAUOUdJRSlIwEaGlnaJRoHiiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBZLA4WUaCF0lFKUjA1ib3VuZGVkX2JlbG93lGgeKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIXSUUpSMDWJvdW5kZWRfYWJvdmWUaB4olgMAAAAAAAAAAQEBlGgtSwOFlGghdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBZoGUsDhZRoG2geKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoIXSUUpRoJGgeKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFksDhZRoIXSUUpRoKWgeKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoM2geKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoOE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBlLBoWUaBtoHiiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCF0lFKUaCRoHiiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCF0lFKUaCloHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDNoHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDhOdWJ1aBlOaBBOaDhOdWIu", "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (764 kB). View file
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward": -
|
|
|
1 |
+
{"mean_reward": -1.8691898850607687, "std_reward": 0.7625448181906248, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-20T16:10:10.215016"}
|
vec_normalize.pkl
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:092346adf5d383fe36caf2175c345ab9cec44cde58e4269b8d8e6a1fa4e72afd
|
3 |
+
size 2387
|