Hugging Face's logo

ernestumorga
/
ppo-seals-Humanoid-v0

Reinforcement Learning
stable-baselines3
seals/Humanoid-v0
deep-reinforcement-learning
Eval Results
Model card Files Community
ernestum commited on
Commit
676cdf1
•
1 Parent(s): ccfe7c0

Initial Commit

Browse files
Files changed (15) hide show
  1. .gitattributes +2 -0
  2. README.md +71 -0
  3. args.yml +75 -0
  4. config.yml +31 -0
  5. env_kwargs.yml +1 -0
  6. ppo-seals/Humanoid-v0.zip +3 -0
  7. ppo-seals/Humanoid-v0/_stable_baselines3_version +1 -0
  8. ppo-seals/Humanoid-v0/data +118 -0
  9. ppo-seals/Humanoid-v0/policy.optimizer.pth +3 -0
  10. ppo-seals/Humanoid-v0/policy.pth +3 -0
  11. ppo-seals/Humanoid-v0/pytorch_variables.pth +3 -0
  12. ppo-seals/Humanoid-v0/system_info.txt +7 -0
  13. results.json +1 -0
  14. train_eval_metrics.zip +3 -0
  15. vec_normalize.pkl +3 -0
.gitattributes CHANGED
@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
25
  *.zip filter=lfs diff=lfs merge=lfs -text
26
  *.zstandard filter=lfs diff=lfs merge=lfs -text
27
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
25
  *.zip filter=lfs diff=lfs merge=lfs -text
26
  *.zstandard filter=lfs diff=lfs merge=lfs -text
27
  *tfevents* filter=lfs diff=lfs merge=lfs -text
28
+ *.mp4 filter=lfs diff=lfs merge=lfs -text
29
+ vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - seals/Humanoid-v0
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: PPO
10
+ results:
11
+ - metrics:
12
+ - type: mean_reward
13
+ value: -43.69 +/- 155.83
14
+ name: mean_reward
15
+ task:
16
+ type: reinforcement-learning
17
+ name: reinforcement-learning
18
+ dataset:
19
+ name: seals/Humanoid-v0
20
+ type: seals/Humanoid-v0
21
+ ---
22
+
23
+ # **PPO** Agent playing **seals/Humanoid-v0**
24
+ This is a trained model of a **PPO** agent playing **seals/Humanoid-v0**
25
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
26
+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
27
+
28
+ The RL Zoo is a training framework for Stable Baselines3
29
+ reinforcement learning agents,
30
+ with hyperparameter optimization and pre-trained agents included.
31
+
32
+ ## Usage (with SB3 RL Zoo)
33
+
34
+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
35
+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
36
+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
37
+
38
+ ```
39
+ # Download model and save it into the logs/ folder
40
+ python -m utils.load_from_hub --algo ppo --env seals/Humanoid-v0 -orga ernestumorga -f logs/
41
+ python enjoy.py --algo ppo --env seals/Humanoid-v0 -f logs/
42
+ ```
43
+
44
+ ## Training (with the RL Zoo)
45
+ ```
46
+ python train.py --algo ppo --env seals/Humanoid-v0 -f logs/
47
+ # Upload the model and generate video (when possible)
48
+ python -m utils.push_to_hub --algo ppo --env seals/Humanoid-v0 -f logs/ -orga ernestumorga
49
+ ```
50
+
51
+ ## Hyperparameters
52
+ ```python
53
+ OrderedDict([('batch_size', 256),
54
+ ('clip_range', 0.2),
55
+ ('ent_coef', 2.0745206045994986e-05),
56
+ ('gae_lambda', 0.92),
57
+ ('gamma', 0.999),
58
+ ('learning_rate', 2.0309225666232827e-05),
59
+ ('max_grad_norm', 0.5),
60
+ ('n_envs', 1),
61
+ ('n_epochs', 20),
62
+ ('n_steps', 2048),
63
+ ('n_timesteps', 10000000.0),
64
+ ('normalize', True),
65
+ ('policy', 'MlpPolicy'),
66
+ ('policy_kwargs',
67
+ 'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
68
+ 'vf=[256, 256])])'),
69
+ ('vf_coef', 0.819262464558427),
70
+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
71
+ ```
args.yml ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ !!python/object/apply:collections.OrderedDict
2
+ - - - algo
3
+ - ppo
4
+ - - device
5
+ - cpu
6
+ - - env
7
+ - seals/Humanoid-v0
8
+ - - env_kwargs
9
+ - null
10
+ - - eval_episodes
11
+ - 5
12
+ - - eval_freq
13
+ - 25000
14
+ - - gym_packages
15
+ - []
16
+ - - hyperparams
17
+ - null
18
+ - - log_folder
19
+ - seals_experts
20
+ - - log_interval
21
+ - -1
22
+ - - n_eval_envs
23
+ - 1
24
+ - - n_evaluations
25
+ - null
26
+ - - n_jobs
27
+ - 1
28
+ - - n_startup_trials
29
+ - 10
30
+ - - n_timesteps
31
+ - -1
32
+ - - n_trials
33
+ - 500
34
+ - - no_optim_plots
35
+ - false
36
+ - - num_threads
37
+ - 4
38
+ - - optimization_log_path
39
+ - null
40
+ - - optimize_hyperparameters
41
+ - false
42
+ - - pruner
43
+ - median
44
+ - - sampler
45
+ - tpe
46
+ - - save_freq
47
+ - -1
48
+ - - save_replay_buffer
49
+ - false
50
+ - - seed
51
+ - 4130770000
52
+ - - storage
53
+ - null
54
+ - - study_name
55
+ - null
56
+ - - tensorboard_log
57
+ - ''
58
+ - - total_n_trials
59
+ - null
60
+ - - track
61
+ - false
62
+ - - trained_agent
63
+ - ''
64
+ - - truncate_last_trajectory
65
+ - true
66
+ - - uuid
67
+ - false
68
+ - - vec_env
69
+ - dummy
70
+ - - verbose
71
+ - 1
72
+ - - wandb_entity
73
+ - null
74
+ - - wandb_project_name
75
+ - sb3
config.yml ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ !!python/object/apply:collections.OrderedDict
2
+ - - - batch_size
3
+ - 256
4
+ - - clip_range
5
+ - 0.2
6
+ - - ent_coef
7
+ - 2.0745206045994986e-05
8
+ - - gae_lambda
9
+ - 0.92
10
+ - - gamma
11
+ - 0.999
12
+ - - learning_rate
13
+ - 2.0309225666232827e-05
14
+ - - max_grad_norm
15
+ - 0.5
16
+ - - n_envs
17
+ - 1
18
+ - - n_epochs
19
+ - 20
20
+ - - n_steps
21
+ - 2048
22
+ - - n_timesteps
23
+ - 10000000.0
24
+ - - normalize
25
+ - true
26
+ - - policy
27
+ - MlpPolicy
28
+ - - policy_kwargs
29
+ - dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], vf=[256, 256])])
30
+ - - vf_coef
31
+ - 0.819262464558427
env_kwargs.yml ADDED
@@ -0,0 +1 @@
 
 
1
+ {}
ppo-seals/Humanoid-v0.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3afc028b1fa00c19522162490f45d85b6fefb50b826699fb9c580dc20843dde0
3
+ size 4016758
ppo-seals/Humanoid-v0/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.5.0
ppo-seals/Humanoid-v0/data ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__doc__": "\n 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\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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7fcf31182f70>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf31186040>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf311860d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf31186160>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fcf311861f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fcf31186280>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf31186310>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fcf311863a0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf31186430>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf311864c0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf31186550>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fcf3117c840>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {
23
+ ":type:": "<class 'dict'>",
24
+ ":serialized:": "gAWVbAAAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARSZUxVlJOUjAhuZXRfYXJjaJRdlH2UKIwCcGmUXZQoTQABTQABZYwCdmaUXZQoTQABTQABZXVhdS4=",
25
+ "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>",
26
+ "net_arch": [
27
+ {
28
+ "pi": [
29
+ 256,
30
+ 256
31
+ ],
32
+ "vf": [
33
+ 256,
34
+ 256
35
+ ]
36
+ }
37
+ ]
38
+ },
39
+ "observation_space": {
40
+ ":type:": "<class 'gym.spaces.box.Box'>",
41
+ ":serialized:": "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",
42
+ "dtype": "float64",
43
+ "_shape": [
44
+ 378
45
+ ],
46
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
47
+ "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]",
48
+ "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False]",
49
+ "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False False False]",
50
+ "_np_random": null
51
+ },
52
+ "action_space": {
53
+ ":type:": "<class 'gym.spaces.box.Box'>",
54
+ ":serialized:": "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",
55
+ "dtype": "float32",
56
+ "_shape": [
57
+ 17
58
+ ],
59
+ "low": "[-0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4\n -0.4 -0.4 -0.4]",
60
+ "high": "[0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4]",
61
+ "bounded_below": "[ True True True True True True True True True True True True\n True True True True True]",
62
+ "bounded_above": "[ True True True True True True True True True True True True\n True True True True True]",
63
+ "_np_random": "RandomState(MT19937)"
64
+ },
65
+ "n_envs": 1,
66
+ "num_timesteps": 10000384,
67
+ "_total_timesteps": 10000000,
68
+ "_num_timesteps_at_start": 0,
69
+ "seed": 0,
70
+ "action_noise": null,
71
+ "start_time": 1651240813.3220909,
72
+ "learning_rate": {
73
+ ":type:": "<class 'function'>",
74
+ ":serialized:": "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"
75
+ },
76
+ "tensorboard_log": null,
77
+ "lr_schedule": {
78
+ ":type:": "<class 'function'>",
79
+ ":serialized:": "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"
80
+ },
81
+ "_last_obs": null,
82
+ "_last_episode_starts": {
83
+ ":type:": "<class 'numpy.ndarray'>",
84
+ ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
85
+ },
86
+ "_last_original_obs": {
87
+ ":type:": "<class 'numpy.ndarray'>",
88
+ ":serialized:": "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"
89
+ },
90
+ "_episode_num": 0,
91
+ "use_sde": false,
92
+ "sde_sample_freq": -1,
93
+ "_current_progress_remaining": -3.8399999999993994e-05,
94
+ "ep_info_buffer": {
95
+ ":type:": "<class 'collections.deque'>",
96
+ ":serialized:": "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"
97
+ },
98
+ "ep_success_buffer": {
99
+ ":type:": "<class 'collections.deque'>",
100
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
101
+ },
102
+ "_n_updates": 97660,
103
+ "n_steps": 2048,
104
+ "gamma": 0.999,
105
+ "gae_lambda": 0.92,
106
+ "ent_coef": 2.0745206045994986e-05,
107
+ "vf_coef": 0.819262464558427,
108
+ "max_grad_norm": 0.5,
109
+ "batch_size": 256,
110
+ "n_epochs": 20,
111
+ "clip_range": {
112
+ ":type:": "<class 'function'>",
113
+ ":serialized:": "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"
114
+ },
115
+ "clip_range_vf": null,
116
+ "normalize_advantage": true,
117
+ "target_kl": null
118
+ }
ppo-seals/Humanoid-v0/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cbb1ca1f1330d0576547352df83500c0a8e52c2e70ae23efd93038e05fc6e917
3
+ size 2649047
ppo-seals/Humanoid-v0/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:acc136852b4c1bafb6146dc993a3788809518c31de2b018461681e9ef4fcb4db
3
+ size 1325374
ppo-seals/Humanoid-v0/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
ppo-seals/Humanoid-v0/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ OS: Linux-5.4.0-109-generic-x86_64-with-glibc2.29 #123-Ubuntu SMP Fri Apr 8 09:10:54 UTC 2022
2
+ Python: 3.8.10
3
+ Stable-Baselines3: 1.5.0
4
+ PyTorch: 1.11.0+cu102
5
+ GPU Enabled: False
6
+ Numpy: 1.22.3
7
+ Gym: 0.21.0
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -43.691728499999996, "std_reward": 155.83102985637362, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-30T14:02:24.178138"}
train_eval_metrics.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:00fa2021a67e3f3b8f3d02e44d2a7b4f7ef83a90db37824d4a3750674dd2a71d
3
+ size 336752
vec_normalize.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:19aa2c418a343da6b4fbbb73471fc718bab26515a840a6d64c168be356af7c76
3
+ size 20029