Commit
·
e10a701
1
Parent(s):
01f6767
Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +14 -16
- a2c-PandaReachDense-v2/policy.optimizer.pth +2 -2
- a2c-PandaReachDense-v2/policy.pth +2 -2
- a2c-PandaReachDense-v2/system_info.txt +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: -1.27 +/- 0.22
|
| 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 sha256:47d509eabc7e5e25734bd792412ac6c1f722bd3ca130c0b0f405e793dcbbd87b
|
| 3 |
+
size 107731
|
a2c-PandaReachDense-v2/data
CHANGED
|
@@ -4,16 +4,14 @@
|
|
| 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": {
|
| 13 |
":type:": "<class 'dict'>",
|
| 14 |
-
":serialized:": "
|
| 15 |
-
"log_std_init": -2,
|
| 16 |
-
"ortho_init": false,
|
| 17 |
"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
|
| 18 |
"optimizer_kwargs": {
|
| 19 |
"alpha": 0.99,
|
|
@@ -48,19 +46,19 @@
|
|
| 48 |
"_num_timesteps_at_start": 0,
|
| 49 |
"seed": null,
|
| 50 |
"action_noise": null,
|
| 51 |
-
"start_time":
|
| 52 |
-
"learning_rate":
|
| 53 |
"tensorboard_log": null,
|
| 54 |
"lr_schedule": {
|
| 55 |
":type:": "<class 'function'>",
|
| 56 |
-
":serialized:": "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
|
| 57 |
},
|
| 58 |
"_last_obs": {
|
| 59 |
":type:": "<class 'collections.OrderedDict'>",
|
| 60 |
-
":serialized:": "
|
| 61 |
-
"achieved_goal": "[[
|
| 62 |
-
"desired_goal": "[[-
|
| 63 |
-
"observation": "[[
|
| 64 |
},
|
| 65 |
"_last_episode_starts": {
|
| 66 |
":type:": "<class 'numpy.ndarray'>",
|
|
@@ -68,9 +66,9 @@
|
|
| 68 |
},
|
| 69 |
"_last_original_obs": {
|
| 70 |
":type:": "<class 'collections.OrderedDict'>",
|
| 71 |
-
":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
| 72 |
"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]]",
|
| 73 |
-
"desired_goal": "[[
|
| 74 |
"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]]"
|
| 75 |
},
|
| 76 |
"_episode_num": 0,
|
|
@@ -79,7 +77,7 @@
|
|
| 79 |
"_current_progress_remaining": 0.0,
|
| 80 |
"ep_info_buffer": {
|
| 81 |
":type:": "<class 'collections.deque'>",
|
| 82 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
| 83 |
},
|
| 84 |
"ep_success_buffer": {
|
| 85 |
":type:": "<class 'collections.deque'>",
|
|
@@ -90,7 +88,7 @@
|
|
| 90 |
"gamma": 0.99,
|
| 91 |
"gae_lambda": 0.9,
|
| 92 |
"ent_coef": 0.0,
|
| 93 |
-
"vf_coef": 0.
|
| 94 |
"max_grad_norm": 0.5,
|
| 95 |
"normalize_advantage": false
|
| 96 |
}
|
|
|
|
| 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 0x7f5eb8bd5550>",
|
| 8 |
"__abstractmethods__": "frozenset()",
|
| 9 |
+
"_abc_impl": "<_abc_data object at 0x7f5eb8bc9db0>"
|
| 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,
|
|
|
|
| 46 |
"_num_timesteps_at_start": 0,
|
| 47 |
"seed": null,
|
| 48 |
"action_noise": null,
|
| 49 |
+
"start_time": 1678178448352052116,
|
| 50 |
+
"learning_rate": 5e-05,
|
| 51 |
"tensorboard_log": null,
|
| 52 |
"lr_schedule": {
|
| 53 |
":type:": "<class 'function'>",
|
| 54 |
+
":serialized:": "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"
|
| 55 |
},
|
| 56 |
"_last_obs": {
|
| 57 |
":type:": "<class 'collections.OrderedDict'>",
|
| 58 |
+
":serialized:": "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",
|
| 59 |
+
"achieved_goal": "[[0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]]",
|
| 60 |
+
"desired_goal": "[[-0.07205848 1.3371546 -0.5547636 ]\n [-0.23663571 0.6614635 0.05611248]\n [-1.0846347 -0.03011797 1.2200181 ]\n [ 1.015283 1.6101891 -0.2507116 ]]",
|
| 61 |
+
"observation": "[[0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]]"
|
| 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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAqga8vRp7+D2kETA+UmP9PHjYkb0QcOI8eqmsPIBmAT1JsFw+M6Y3vblX8LxtcDk9lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
|
| 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": "[[-0.09180959 0.12132855 0.1719423 ]\n [ 0.03093115 -0.07121366 0.02764133]\n [ 0.02107691 0.03159189 0.21551622]\n [-0.04483623 -0.0293387 0.04527323]]",
|
| 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'>",
|
|
|
|
| 88 |
"gamma": 0.99,
|
| 89 |
"gae_lambda": 0.9,
|
| 90 |
"ent_coef": 0.0,
|
| 91 |
+
"vf_coef": 0.5,
|
| 92 |
"max_grad_norm": 0.5,
|
| 93 |
"normalize_advantage": false
|
| 94 |
}
|
a2c-PandaReachDense-v2/policy.optimizer.pth
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:d3776255a6b09adfc0afa939b960ef125fb01fc8b684117958ec3a7062ff94c3
|
| 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:ce9b9d16fe4291eea069cfdd4db748ea0c862262a97bbba98bbc1dd9c586929d
|
| 3 |
+
size 45886
|
a2c-PandaReachDense-v2/system_info.txt
CHANGED
|
@@ -2,6 +2,6 @@
|
|
| 2 |
- Python: 3.8.10
|
| 3 |
- Stable-Baselines3: 1.7.0
|
| 4 |
- PyTorch: 1.13.1+cu116
|
| 5 |
-
- GPU Enabled:
|
| 6 |
- Numpy: 1.22.4
|
| 7 |
- Gym: 0.21.0
|
|
|
|
| 2 |
- Python: 3.8.10
|
| 3 |
- Stable-Baselines3: 1.7.0
|
| 4 |
- PyTorch: 1.13.1+cu116
|
| 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 0x7fa0e242a4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa0e241fcf0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "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": 1678107601629294937, "learning_rate": 0.0005, "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": "[[ 2.0824036e-01 -3.2277207e-04 5.2470660e-01]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01]]", "desired_goal": "[[-1.4402509 1.1556531 -0.80143285]\n [-1.0520303 -0.5526696 0.40329814]\n [-0.12755902 0.20955367 1.3801124 ]\n [ 0.3698503 -1.6835413 1.7184536 ]]", "observation": "[[ 2.0824036e-01 -3.2277207e-04 5.2470660e-01 1.5015540e-02\n -1.4876602e-03 3.3611685e-02]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01 1.5015540e-02\n -1.4876602e-03 3.3611685e-02]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01 1.5015540e-02\n -1.4876602e-03 3.3611685e-02]\n [ 2.0824036e-01 -3.2277207e-04 5.2470660e-01 1.5015540e-02\n -1.4876602e-03 3.3611685e-02]]"}, "_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": "[[ 0.05278885 -0.08316813 0.06722453]\n [-0.01192655 0.10865656 0.2448897 ]\n [-0.06792582 -0.1046132 0.06978957]\n [-0.11709457 0.06164816 0.29514715]]", "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": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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 0x7f5eb8bd5550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5eb8bc9db0>"}, "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": 1678178448352052116, "learning_rate": 5e-05, "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": "[[0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]\n [0.23159078 0.0118631 0.5324734 ]]", "desired_goal": "[[-0.07205848 1.3371546 -0.5547636 ]\n [-0.23663571 0.6614635 0.05611248]\n [-1.0846347 -0.03011797 1.2200181 ]\n [ 1.015283 1.6101891 -0.2507116 ]]", "observation": "[[0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]\n [0.23159078 0.0118631 0.5324734 0.02575705 0.00180242 0.02586727]]"}, "_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": "[[-0.09180959 0.12132855 0.1719423 ]\n [ 0.03093115 -0.07121366 0.02764133]\n [ 0.02107691 0.03159189 0.21551622]\n [-0.04483623 -0.0293387 0.04527323]]", "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": 0.9, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "False", "Numpy": "1.22.4", "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": -1.272870457242243, "std_reward": 0.21837125138098704, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-07T09:41:57.836415"}
|
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 sha256:9d312e44b935aba750f1ea422c173f9feb57c57b58fdf5a9699cbd3714c07041
|
| 3 |
size 3056
|