Commit
•
0b77ac0
1
Parent(s):
7a4d8ce
Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +1 -1
- a2c-PandaReachDense-v2/data +10 -10
- 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: -2.00 +/- 0.45
|
| 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 108021
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b54871c916c404db560bfff171b7f209a1b36ed80e16c4983492e6df01969360
|
| 3 |
size 108021
|
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": 0,
|
| 12 |
"policy_kwargs": {
|
|
@@ -46,7 +46,7 @@
|
|
| 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": {
|
|
@@ -55,10 +55,10 @@
|
|
| 55 |
},
|
| 56 |
"_last_obs": {
|
| 57 |
":type:": "<class 'collections.OrderedDict'>",
|
| 58 |
-
":serialized:": "
|
| 59 |
-
"achieved_goal": "[[ 0.
|
| 60 |
-
"desired_goal": "[[
|
| 61 |
-
"observation": "[[ 0.
|
| 62 |
},
|
| 63 |
"_last_episode_starts": {
|
| 64 |
":type:": "<class 'numpy.ndarray'>",
|
|
@@ -66,9 +66,9 @@
|
|
| 66 |
},
|
| 67 |
"_last_original_obs": {
|
| 68 |
":type:": "<class 'collections.OrderedDict'>",
|
| 69 |
-
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
| 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": "[[
|
| 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,
|
|
@@ -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 0x7f137d2a1af0>",
|
| 8 |
"__abstractmethods__": "frozenset()",
|
| 9 |
+
"_abc_impl": "<_abc_data object at 0x7f137d29a9f0>"
|
| 10 |
},
|
| 11 |
"verbose": 0,
|
| 12 |
"policy_kwargs": {
|
|
|
|
| 46 |
"_num_timesteps_at_start": 0,
|
| 47 |
"seed": null,
|
| 48 |
"action_noise": null,
|
| 49 |
+
"start_time": 1677617346212685490,
|
| 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": "[[ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]]",
|
| 60 |
+
"desired_goal": "[[-1.5559986 -0.3084607 0.4633201 ]\n [-1.0064137 0.5959204 0.58259374]\n [-0.8595265 0.26932818 -0.82398283]\n [ 1.1112314 0.24166955 0.42888016]]",
|
| 61 |
+
"observation": "[[ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]]"
|
| 62 |
},
|
| 63 |
"_last_episode_starts": {
|
| 64 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
|
| 66 |
},
|
| 67 |
"_last_original_obs": {
|
| 68 |
":type:": "<class 'collections.OrderedDict'>",
|
| 69 |
+
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA87iSvfqXbjs1nSM+o2HrPc257r3x5j0+MNAKPr66/T24Hcs98/hrvQBglDw9C4o+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
|
| 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": "[[-0.07164183 0.00364065 0.15977938]\n [ 0.11493232 -0.11656532 0.18545128]\n [ 0.1355598 0.12389134 0.09917778]\n [-0.05761046 0.01811218 0.269617 ]]",
|
| 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,
|
|
|
|
| 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 sha256:64e66d93e8637da97b21c6b3a6b880369c81da0ba7d7a5a8d77d987432b26f87
|
| 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:f127c597f4a2b07cf832f3725c0cda428bf47d56f6c5e758a395c983befea679
|
| 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 0x7f9a6094e790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9a609b2e10>"}, "verbose": 0, "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": 750000, "_total_timesteps": 750000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677612588652494617, "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": "[[ 0.2533469 -0.07868704 0.5062111 ]\n [ 0.2533469 -0.07868704 0.5062111 ]\n [ 0.2533469 -0.07868704 0.5062111 ]\n [ 0.2533469 -0.07868704 0.5062111 ]]", "desired_goal": "[[ 0.94232285 1.6880125 -1.5430213 ]\n [ 0.47430393 1.5095447 -0.06458251]\n [-1.5660623 0.8820228 -0.9084561 ]\n [ 0.21140637 -0.7007913 0.89823633]]", "observation": "[[ 0.2533469 -0.07868704 0.5062111 0.02465196 -0.01131226 0.01709374]\n [ 0.2533469 -0.07868704 0.5062111 0.02465196 -0.01131226 0.01709374]\n [ 0.2533469 -0.07868704 0.5062111 0.02465196 -0.01131226 0.01709374]\n [ 0.2533469 -0.07868704 0.5062111 0.02465196 -0.01131226 0.01709374]]"}, "_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": "[[ 0.00147547 -0.05791936 0.14557342]\n [ 0.1008561 -0.13638765 0.23392038]\n [ 0.01362938 -0.0929257 0.05810409]\n [ 0.11618671 -0.01054786 0.19189546]]", "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": 37500, "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": "1.22.4", "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 0x7f137d2a1af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f137d29a9f0>"}, "verbose": 0, "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": 750000, "_total_timesteps": 750000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677617346212685490, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA0MrYPoxcnrwonAg/0MrYPoxcnrwonAg/0MrYPoxcnrwonAg/0MrYPoxcnrwonAg/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA9irHv5Dunb5LOO0+KtKAvz2OGD/dJBU/7glcv2LliT6K8FK/1TyOPzl4dz4ults+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADQytg+jFyevCicCD/6mNY6epDtuhbnCzvQytg+jFyevCicCD/6mNY6epDtuhbnCzvQytg+jFyevCicCD/6mNY6epDtuhbnCzvQytg+jFyevCicCD/6mNY6epDtuhbnCzuUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]\n [ 0.42342234 -0.01933124 0.53363276]]", "desired_goal": "[[-1.5559986 -0.3084607 0.4633201 ]\n [-1.0064137 0.5959204 0.58259374]\n [-0.8595265 0.26932818 -0.82398283]\n [ 1.1112314 0.24166955 0.42888016]]", "observation": "[[ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]\n [ 0.42342234 -0.01933124 0.53363276 0.00163725 -0.00181247 0.00213475]]"}, "_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": "[[-0.07164183 0.00364065 0.15977938]\n [ 0.11493232 -0.11656532 0.18545128]\n [ 0.1355598 0.12389134 0.09917778]\n [-0.05761046 0.01811218 0.269617 ]]", "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": 37500, "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": "1.22.4", "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": -2.000701599055901, "std_reward": 0.4502172418246482, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T21:28:56.589839"}
|
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:de7932f12502a6513d568bb0d45a0997329e2c0ca3f80bc179fc33009363701a
|
| 3 |
size 3056
|