Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +16 -16
- a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
- a2c-PandaReachDense-v2/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
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: -2.03 +/- 0.33
|
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 sha256:14a1cf983235ae3f6e20431e01b83c1ace51c766fcdfa2ea0a9c166ee17d2208
|
3 |
+
size 108107
|
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": {
|
@@ -40,13 +40,13 @@
|
|
40 |
"bounded_above": "[ True True True]",
|
41 |
"_np_random": null
|
42 |
},
|
43 |
-
"n_envs":
|
44 |
-
"num_timesteps":
|
45 |
-
"_total_timesteps":
|
46 |
"_num_timesteps_at_start": 0,
|
47 |
"seed": null,
|
48 |
"action_noise": null,
|
49 |
-
"start_time":
|
50 |
"learning_rate": 0.0007,
|
51 |
"tensorboard_log": null,
|
52 |
"lr_schedule": {
|
@@ -55,21 +55,21 @@
|
|
55 |
},
|
56 |
"_last_obs": {
|
57 |
":type:": "<class 'collections.OrderedDict'>",
|
58 |
-
":serialized:": "
|
59 |
-
"achieved_goal": "[[
|
60 |
-
"desired_goal": "[[
|
61 |
-
"observation": "[[ 4.
|
62 |
},
|
63 |
"_last_episode_starts": {
|
64 |
":type:": "<class 'numpy.ndarray'>",
|
65 |
-
":serialized:": "
|
66 |
},
|
67 |
"_last_original_obs": {
|
68 |
":type:": "<class 'collections.OrderedDict'>",
|
69 |
-
":serialized:": "
|
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.
|
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,
|
@@ -77,7 +77,7 @@
|
|
77 |
"_current_progress_remaining": 0.0,
|
78 |
"ep_info_buffer": {
|
79 |
":type:": "<class 'collections.deque'>",
|
80 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
81 |
},
|
82 |
"ep_success_buffer": {
|
83 |
":type:": "<class 'collections.deque'>",
|
|
|
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 0x7ff3410ff280>",
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc_data object at 0x7ff3410fa690>"
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
|
|
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": 1675531582609191243,
|
50 |
"learning_rate": 0.0007,
|
51 |
"tensorboard_log": null,
|
52 |
"lr_schedule": {
|
|
|
55 |
},
|
56 |
"_last_obs": {
|
57 |
":type:": "<class 'collections.OrderedDict'>",
|
58 |
+
":serialized:": "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",
|
59 |
+
"achieved_goal": "[[ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]]",
|
60 |
+
"desired_goal": "[[-0.8869353 0.23731653 0.0848301 ]\n [ 0.6493383 0.07202187 1.492992 ]\n [-0.19209035 1.2582282 -1.6293697 ]\n [ 0.7582897 0.7604692 0.8874359 ]]",
|
61 |
+
"observation": "[[ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-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": "[[ 0.04038778 -0.05583432 0.26914564]\n [ 0.14525157 0.06572128 0.15881503]\n [-0.07644401 -0.05342935 0.11837809]\n [ 0.03129156 0.09528251 0.06571158]]",
|
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,
|
|
|
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'>",
|
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 44734
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5e46a47eb55b65088ff38b7da40aa3664a9e8b5160afc745710df379ce120686
|
3 |
size 44734
|
a2c-PandaReachDense-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 46014
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:27ea0f8ad57a253d0af8449165bae9ea8dcf880d7968361a4441edeac01aafa9
|
3 |
size 46014
|
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 0x7f287f873ee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f287f8692d0>"}, "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": 8, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675424801071000386, "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": "[[0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]\n [0.42586228 0.01253699 0.56561273]]", "desired_goal": "[[ 0.82409817 1.215523 -1.6275438 ]\n [-1.35917 -0.13277175 -0.91187465]\n [-1.6586834 0.05185289 1.3795464 ]\n [-0.5163744 -1.730141 -0.6945469 ]\n [-0.32144597 -1.2495009 -1.3482828 ]\n [-0.48934466 0.55941355 0.8939363 ]\n [-1.2716123 0.2998407 -1.5728962 ]\n [ 1.0012443 0.4113592 -0.5258426 ]]", "observation": "[[ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]\n [ 4.2586228e-01 1.2536993e-02 5.6561273e-01 -6.3102054e-03\n -5.2643241e-04 1.9891241e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAEBAQEBAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="}, "_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]\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]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[ 0.11472806 0.00116462 0.28087756]\n [ 0.04373524 -0.06150021 0.10354829]\n [-0.02084158 -0.01910262 0.00907165]\n [-0.05897748 0.07260305 0.1780269 ]\n [ 0.10908879 -0.0947272 0.2445363 ]\n [ 0.09571612 0.07766444 0.2295397 ]\n [-0.05601726 -0.11540248 0.1997207 ]\n [-0.08796424 0.06970382 0.02613764]]", "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]\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]\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"}}
|
|
|
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 0x7ff3410ff280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff3410fa690>"}, "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": 1675531582609191243, "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": "[[ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]\n [ 0.4114801 -0.00572893 0.53892666]]", "desired_goal": "[[-0.8869353 0.23731653 0.0848301 ]\n [ 0.6493383 0.07202187 1.492992 ]\n [-0.19209035 1.2582282 -1.6293697 ]\n [ 0.7582897 0.7604692 0.8874359 ]]", "observation": "[[ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-03]\n [ 4.1148010e-01 -5.7289274e-03 5.3892666e-01 7.5443494e-03\n 2.9920516e-04 2.6197578e-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": "[[ 0.04038778 -0.05583432 0.26914564]\n [ 0.14525157 0.06572128 0.15881503]\n [-0.07644401 -0.05342935 0.11837809]\n [ 0.03129156 0.09528251 0.06571158]]", "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
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward": -
|
|
|
1 |
+
{"mean_reward": -2.0325827070046216, "std_reward": 0.3348441669761919, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-04T18:20:44.278959"}
|
vec_normalize.pkl
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 3056
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:119d9fd23439dfb9ff29808826981aa3dae0f4a784f701a92411f051620bdae0
|
3 |
size 3056
|