emmashe15 commited on
Commit
7c5c4aa
1 Parent(s): ba2f2e2

Initial commit

Browse files
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
- value: -1.83 +/- 0.73
20
  name: mean_reward
21
  verified: false
22
  ---
 
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
+ value: -22.12 +/- 9.22
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 sha256:46137a4f83300981cf8b68d6c66e1beb687e0c13881de82c5db2be6400874838
3
- size 108028
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:34a6f193ab65f8cca881dcdeacdc87e5b09366db9b160bbb46ba093412688e54
3
+ size 107891
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 0x7f993d68e550>",
8
  "__abstractmethods__": "frozenset()",
9
- "_abc_impl": "<_abc._abc_data object at 0x7f993d68ca40>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
@@ -41,24 +41,24 @@
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": 1678751015547603070,
50
  "learning_rate": 0.0007,
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": "[[ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]]",
60
- "desired_goal": "[[-1.6999246 -1.6913744 -0.49633312]\n [-0.58880997 1.3871876 -1.1481968 ]\n [ 1.4409754 -1.4682379 -1.6188858 ]\n [ 0.2297837 -1.0013537 0.449207 ]]",
61
- "observation": "[[ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]]"
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:": "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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": "[[-0.07393882 -0.10403971 0.1447616 ]\n [ 0.1430797 -0.12036461 0.24245602]\n [-0.11993095 0.02876073 0.21289231]\n [-0.12466266 0.12075014 0.09526288]]",
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,
@@ -77,13 +77,13 @@
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'>",
84
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
85
  },
86
- "_n_updates": 50000,
87
  "n_steps": 5,
88
  "gamma": 0.99,
89
  "gae_lambda": 1.0,
 
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 0x0000019EE5DCE4C0>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x0000019EE5DCCF00>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
 
41
  "_np_random": null
42
  },
43
  "n_envs": 4,
44
+ "num_timesteps": 3000000,
45
+ "_total_timesteps": 3000000,
46
  "_num_timesteps_at_start": 0,
47
  "seed": null,
48
  "action_noise": null,
49
+ "start_time": 1678754409497661000,
50
  "learning_rate": 0.0007,
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": "[[0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]]",
60
+ "desired_goal": "[[-0.6777542 0.12027428 0.793966 ]\n [ 0.08777167 -0.9872501 0.17718315]\n [ 0.67297673 1.5893114 0.07388794]\n [-1.6354157 1.2329766 0.3476299 ]]",
61
+ "observation": "[[ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]]"
62
  },
63
  "_last_episode_starts": {
64
  ":type:": "<class 'numpy.ndarray'>",
 
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": "[[ 0.01297524 0.0790508 0.2072151 ]\n [ 0.0294119 -0.12817492 0.05431482]\n [ 0.01259224 0.02212919 0.1371811 ]\n [ 0.06738417 -0.12942088 0.0863241 ]]",
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'>",
84
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
85
  },
86
+ "_n_updates": 150000,
87
  "n_steps": 5,
88
  "gamma": 0.99,
89
  "gae_lambda": 1.0,
a2c-PandaReachDense-v2/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:afdcab4731f16520943ab6e4e0a0611c288f4d31110b762070d1b4829fbd2380
3
  size 44734
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c39d5d1310c10b08ee99d05fbb6ed1ea539d62960130298e25135c72de973fc2
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:f25726195edeafff8654de9e62beae0d5185b3ad9f7aa0ae9dc330520860775f
3
  size 46014
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:66b0704802c1887a40abfce17ae9edf18a4664430860c63b1361c365858cbb3c
3
  size 46014
a2c-PandaReachDense-v2/system_info.txt CHANGED
@@ -1,7 +1,7 @@
1
- - OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
- - Python: 3.9.16
3
  - Stable-Baselines3: 1.7.0
4
- - PyTorch: 1.13.1+cu116
5
  - GPU Enabled: True
6
- - Numpy: 1.22.4
7
  - Gym: 0.21.0
 
1
+ - OS: Windows-10-10.0.25267-SP0 10.0.25267
2
+ - Python: 3.9.13
3
  - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.0+cu117
5
  - GPU Enabled: True
6
+ - Numpy: 1.21.5
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 0x7f993d68e550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f993d68ca40>"}, "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": 1678751015547603070, "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": "[[ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]\n [ 0.45583445 -0.00808279 0.46663055]]", "desired_goal": "[[-1.6999246 -1.6913744 -0.49633312]\n [-0.58880997 1.3871876 -1.1481968 ]\n [ 1.4409754 -1.4682379 -1.6188858 ]\n [ 0.2297837 -1.0013537 0.449207 ]]", "observation": "[[ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]\n [ 0.45583445 -0.00808279 0.46663055 0.01614659 -0.0033271 0.00422035]]"}, "_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": "[[-0.07393882 -0.10403971 0.1447616 ]\n [ 0.1430797 -0.12036461 0.24245602]\n [-0.11993095 0.02876073 0.21289231]\n [-0.12466266 0.12075014 0.09526288]]", "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "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 0x0000019EE5DCE4C0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000019EE5DCCF00>"}, "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": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678754409497661000, "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": "[[0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]\n [0.41598913 0.03421149 0.7885395 ]]", "desired_goal": "[[-0.6777542 0.12027428 0.793966 ]\n [ 0.08777167 -0.9872501 0.17718315]\n [ 0.67297673 1.5893114 0.07388794]\n [-1.6354157 1.2329766 0.3476299 ]]", "observation": "[[ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]\n [ 0.41598913 0.03421149 0.7885395 0.03471812 -0.00321864 0.03949407]]"}, "_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": "[[ 0.01297524 0.0790508 0.2072151 ]\n [ 0.0294119 -0.12817492 0.05431482]\n [ 0.01259224 0.02212919 0.1371811 ]\n [ 0.06738417 -0.12942088 0.0863241 ]]", "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": 150000, "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": "Windows-10-10.0.25267-SP0 10.0.25267", "Python": "3.9.13", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu117", "GPU Enabled": "True", "Numpy": "1.21.5", "Gym": "0.21.0"}}
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": -1.8292761135846376, "std_reward": 0.7311932255541876, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-14T00:40:56.011719"}
 
1
+ {"mean_reward": -22.11944628469646, "std_reward": 9.22340839600128, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-14T12:09:12.953809"}
vec_normalize.pkl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:798fff2da56a9e5f199c4b1c117d2bd3f67d0e4f1631f96cdeaf23fb0a02c82e
3
  size 3056
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9ac0197f3d093a4532427efb7429f13b5822e16cff0764a5e7c46ca5944d63c2
3
  size 3056