Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +22 -22
- 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
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: -1.83 +/- 0.30
|
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:b9783a93ca172da65086e4e98b97bd47b5f8c1fb64687c1b1f13a189e8d6e2ff
|
3 |
+
size 108179
|
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._abc_data object at
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
@@ -19,53 +19,53 @@
|
|
19 |
"weight_decay": 0
|
20 |
}
|
21 |
},
|
22 |
-
"num_timesteps":
|
23 |
-
"_total_timesteps":
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
-
"start_time":
|
28 |
-
"learning_rate": 0.
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
31 |
":type:": "<class 'function'>",
|
32 |
-
":serialized:": "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
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
-
":serialized:": "
|
37 |
-
"achieved_goal": "[[
|
38 |
-
"desired_goal": "[[ 1.
|
39 |
-
"observation": "[[
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
43 |
-
":serialized:": "
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
-
":serialized:": "
|
48 |
-
"achieved_goal": "[[ 3.8439669e-02 -2.
|
49 |
-
"desired_goal": "[[0.
|
50 |
-
"observation": "[[ 3.8439669e-02 -2.
|
51 |
},
|
52 |
"_episode_num": 0,
|
53 |
"use_sde": false,
|
54 |
"sde_sample_freq": -1,
|
55 |
-
"_current_progress_remaining": 0.
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
-
":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
63 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
64 |
},
|
65 |
-
"_n_updates":
|
66 |
"n_steps": 5,
|
67 |
-
"gamma": 0.
|
68 |
-
"gae_lambda":
|
69 |
"ent_coef": 0.0,
|
70 |
"vf_coef": 0.5,
|
71 |
"max_grad_norm": 0.5,
|
@@ -91,5 +91,5 @@
|
|
91 |
"bounded_above": "[ True True True]",
|
92 |
"_np_random": null
|
93 |
},
|
94 |
-
"n_envs":
|
95 |
}
|
|
|
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 0x7f66356cd1f0>",
|
8 |
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f66356cc880>"
|
10 |
},
|
11 |
"verbose": 1,
|
12 |
"policy_kwargs": {
|
|
|
19 |
"weight_decay": 0
|
20 |
}
|
21 |
},
|
22 |
+
"num_timesteps": 2000000,
|
23 |
+
"_total_timesteps": 2000000,
|
24 |
"_num_timesteps_at_start": 0,
|
25 |
"seed": null,
|
26 |
"action_noise": null,
|
27 |
+
"start_time": 1678401034902632000,
|
28 |
+
"learning_rate": 0.0001,
|
29 |
"tensorboard_log": null,
|
30 |
"lr_schedule": {
|
31 |
":type:": "<class 'function'>",
|
32 |
+
":serialized:": "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"
|
33 |
},
|
34 |
"_last_obs": {
|
35 |
":type:": "<class 'collections.OrderedDict'>",
|
36 |
+
":serialized:": "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",
|
37 |
+
"achieved_goal": "[[ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]]",
|
38 |
+
"desired_goal": "[[ 1.5226084 -1.3241928 0.70342624]\n [ 1.1316198 0.23369336 0.75153 ]\n [ 1.2101599 1.2044358 0.56087565]\n [-0.71787524 -0.4313249 -1.1072202 ]]",
|
39 |
+
"observation": "[[ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]]"
|
40 |
},
|
41 |
"_last_episode_starts": {
|
42 |
":type:": "<class 'numpy.ndarray'>",
|
43 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
|
44 |
},
|
45 |
"_last_original_obs": {
|
46 |
":type:": "<class 'collections.OrderedDict'>",
|
47 |
+
":serialized:": "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",
|
48 |
+
"achieved_goal": "[[ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]]",
|
49 |
+
"desired_goal": "[[ 0.02630091 0.09028225 0.11812861]\n [-0.1454261 0.14149916 0.23615427]\n [ 0.14636715 -0.13000962 0.21254948]\n [-0.1224196 -0.01891135 0.1035522 ]]",
|
50 |
+
"observation": "[[ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
|
51 |
},
|
52 |
"_episode_num": 0,
|
53 |
"use_sde": false,
|
54 |
"sde_sample_freq": -1,
|
55 |
+
"_current_progress_remaining": 0.0,
|
56 |
"_stats_window_size": 100,
|
57 |
"ep_info_buffer": {
|
58 |
":type:": "<class 'collections.deque'>",
|
59 |
+
":serialized:": "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"
|
60 |
},
|
61 |
"ep_success_buffer": {
|
62 |
":type:": "<class 'collections.deque'>",
|
63 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
64 |
},
|
65 |
+
"_n_updates": 100000,
|
66 |
"n_steps": 5,
|
67 |
+
"gamma": 0.9,
|
68 |
+
"gae_lambda": 0.98,
|
69 |
"ent_coef": 0.0,
|
70 |
"vf_coef": 0.5,
|
71 |
"max_grad_norm": 0.5,
|
|
|
91 |
"bounded_above": "[ True True True]",
|
92 |
"_np_random": null
|
93 |
},
|
94 |
+
"n_envs": 4
|
95 |
}
|
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 sha256:9e8140838765d101fe69f14626e3d7757e5e2d131b3b39c3b0fc11a4665ae590
|
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 sha256:817f810caa42e1fe11efe3962a9347e0a3ef8d1f802c2f4b50d2764323ec7956
|
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 0x7fd99ea8c700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd99ea8ad80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 957157, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681759395002128868, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVKwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolgwAAAAAAAAAbRlJP7MHgD/4vqG/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcolgwAAAAAAAAA+82XP18E179IV5+/lGgOSwFLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWGAAAAAAAAABtGUk/sweAP/i+ob87Shw+rvrzvp4o1zyUaA5LAUsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.7855442 1.000235 -1.2636404]]", "desired_goal": "[[ 1.1859735 -1.6798209 -1.2448511]]", "observation": "[[ 0.7855442 1.000235 -1.2636404 0.15262692 -0.4765219 0.02626448]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVKwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolgwAAAAAAAAA6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcolgwAAAAAAAAA3FEJPjSF6T3ohmw+lGgOSwFLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWGAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LAUsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[0.13410133 0.1140236 0.23098338]]", "observation": "[[ 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": 0.04284500000000002, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 191431, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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": 1, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "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 0x7f66356cd1f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f66356cc880>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678401034902632000, "learning_rate": 0.0001, "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": "[[ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01]]", "desired_goal": "[[ 1.5226084 -1.3241928 0.70342624]\n [ 1.1316198 0.23369336 0.75153 ]\n [ 1.2101599 1.2044358 0.56087565]\n [-0.71787524 -0.4313249 -1.1072202 ]]", "observation": "[[ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-03]\n [ 4.3157485e-01 -2.5059274e-04 5.5028903e-01 1.1556428e-03\n -1.5604433e-03 9.6319513e-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 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01]]", "desired_goal": "[[ 0.02630091 0.09028225 0.11812861]\n [-0.1454261 0.14149916 0.23615427]\n [ 0.14636715 -0.13000962 0.21254948]\n [-0.1224196 -0.01891135 0.1035522 ]]", "observation": "[[ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1943899e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 100000, "n_steps": 5, "gamma": 0.9, "gae_lambda": 0.98, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "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": -1.8309198130853475, "std_reward": 0.30043420680574906, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-17T21:18:25.926435"}
|