First model
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- replay.mp4 +3 -0
- result_v1.zip +3 -0
- result_v1/_stable_baselines3_version +1 -0
- result_v1/data +94 -0
- result_v1/policy.optimizer.pth +3 -0
- result_v1/policy.pth +3 -0
- result_v1/pytorch_variables.pth +3 -0
- result_v1/system_info.txt +7 -0
- results.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ 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
|
README.md
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
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: 194.32 +/- 74.88
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: LunarLander-v2
|
20 |
+
type: LunarLander-v2
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
25 |
+
|
26 |
+
## Usage (with Stable-baselines3)
|
27 |
+
TODO: Add your code
|
28 |
+
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7fa871caba70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa871cabb00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa871cabb90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa871cabc20>", "_build": "<function ActorCriticPolicy._build at 0x7fa871cabcb0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa871cabd40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa871cabdd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa871cabe60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa871cabef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa871cabf80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa871cb3050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa871d00330>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652210806.8842354, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 132, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f37080ac0ea25ab06fab8a62607c451aa758b4271f2b27c074eeb98ecf04f331
|
3 |
+
size 261014
|
result_v1.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:074b7835cfd9c99b6066cedbe13e6832b938670e2fda804dfbd3c51fc44585bf
|
3 |
+
size 144044
|
result_v1/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
result_v1/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7fa871caba70>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa871cabb00>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa871cabb90>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa871cabc20>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fa871cabcb0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fa871cabd40>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa871cabdd0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fa871cabe60>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa871cabef0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa871cabf80>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa871cb3050>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fa871d00330>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "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",
|
26 |
+
"dtype": "float32",
|
27 |
+
"_shape": [
|
28 |
+
8
|
29 |
+
],
|
30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
32 |
+
"bounded_below": "[False False False False False False False False]",
|
33 |
+
"bounded_above": "[False False False False False False False False]",
|
34 |
+
"_np_random": null
|
35 |
+
},
|
36 |
+
"action_space": {
|
37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
39 |
+
"n": 4,
|
40 |
+
"_shape": [],
|
41 |
+
"dtype": "int64",
|
42 |
+
"_np_random": null
|
43 |
+
},
|
44 |
+
"n_envs": 16,
|
45 |
+
"num_timesteps": 507904,
|
46 |
+
"_total_timesteps": 500000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1652210806.8842354,
|
51 |
+
"learning_rate": 0.0003,
|
52 |
+
"tensorboard_log": null,
|
53 |
+
"lr_schedule": {
|
54 |
+
":type:": "<class 'function'>",
|
55 |
+
":serialized:": "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"
|
56 |
+
},
|
57 |
+
"_last_obs": {
|
58 |
+
":type:": "<class 'numpy.ndarray'>",
|
59 |
+
":serialized:": "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"
|
60 |
+
},
|
61 |
+
"_last_episode_starts": {
|
62 |
+
":type:": "<class 'numpy.ndarray'>",
|
63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.015808000000000044,
|
70 |
+
"ep_info_buffer": {
|
71 |
+
":type:": "<class 'collections.deque'>",
|
72 |
+
":serialized:": "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"
|
73 |
+
},
|
74 |
+
"ep_success_buffer": {
|
75 |
+
":type:": "<class 'collections.deque'>",
|
76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
+
},
|
78 |
+
"_n_updates": 132,
|
79 |
+
"n_steps": 1024,
|
80 |
+
"gamma": 0.999,
|
81 |
+
"gae_lambda": 0.98,
|
82 |
+
"ent_coef": 0.01,
|
83 |
+
"vf_coef": 0.5,
|
84 |
+
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 64,
|
86 |
+
"n_epochs": 4,
|
87 |
+
"clip_range": {
|
88 |
+
":type:": "<class 'function'>",
|
89 |
+
":serialized:": "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"
|
90 |
+
},
|
91 |
+
"clip_range_vf": null,
|
92 |
+
"normalize_advantage": true,
|
93 |
+
"target_kl": null
|
94 |
+
}
|
result_v1/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8ff805fafbcec84b16f50399af9214a2a173904a0242913e237da8abc4f0cf27
|
3 |
+
size 84829
|
result_v1/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:be927ae418ac47ab80bf25bf34c40bdea7164574bc5f62c4e43b8891d27e5555
|
3 |
+
size 43201
|
result_v1/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
result_v1/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.11.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 194.31749013486095, "std_reward": 74.88264605703014, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T20:05:31.177853"}
|