ChaoChao2023
commited on
Commit
•
faa0a94
1
Parent(s):
28fdae9
上传月球着陆器强化学习模型
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +99 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +9 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: 214.49 +/- 81.86
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
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 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 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 ActorCriticPolicy.__init__ at 0x78d90d2acf70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78d90d2ad000>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78d90d2ad090>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78d90d2ad120>", "_build": "<function ActorCriticPolicy._build at 0x78d90d2ad1b0>", "forward": "<function ActorCriticPolicy.forward at 0x78d90d2ad240>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78d90d2ad2d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78d90d2ad360>", "_predict": "<function ActorCriticPolicy._predict at 0x78d90d2ad3f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78d90d2ad480>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78d90d2ad510>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78d90d2ad5a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78d90d451e00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1705977103540700868, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM0UYTxck2s9JoBZvWzOHL6Fu2y9g/JhvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0930c821a4bc063cdadf41236060bfaff8c57f36567c3d52706e2fb0a8240d5e
|
3 |
+
size 147354
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.0.0a5
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 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 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 ActorCriticPolicy.__init__ at 0x78d90d2acf70>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78d90d2ad000>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78d90d2ad090>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78d90d2ad120>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x78d90d2ad1b0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x78d90d2ad240>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x78d90d2ad2d0>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78d90d2ad360>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x78d90d2ad3f0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78d90d2ad480>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78d90d2ad510>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x78d90d2ad5a0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x78d90d451e00>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 1000448,
|
25 |
+
"_total_timesteps": 1000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": null,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1705977103540700868,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM0UYTxck2s9JoBZvWzOHL6Fu2y9g/JhvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
35 |
+
},
|
36 |
+
"_last_episode_starts": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
39 |
+
},
|
40 |
+
"_last_original_obs": null,
|
41 |
+
"_episode_num": 0,
|
42 |
+
"use_sde": false,
|
43 |
+
"sde_sample_freq": -1,
|
44 |
+
"_current_progress_remaining": -0.00044800000000000395,
|
45 |
+
"_stats_window_size": 100,
|
46 |
+
"ep_info_buffer": {
|
47 |
+
":type:": "<class 'collections.deque'>",
|
48 |
+
":serialized:": "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"
|
49 |
+
},
|
50 |
+
"ep_success_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
+
},
|
54 |
+
"_n_updates": 3908,
|
55 |
+
"observation_space": {
|
56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
+
":serialized:": "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",
|
58 |
+
"dtype": "float32",
|
59 |
+
"bounded_below": "[ True True True True True True True True]",
|
60 |
+
"bounded_above": "[ True True True True True True True True]",
|
61 |
+
"_shape": [
|
62 |
+
8
|
63 |
+
],
|
64 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
65 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
66 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
68 |
+
"_np_random": null
|
69 |
+
},
|
70 |
+
"action_space": {
|
71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
|
73 |
+
"n": "4",
|
74 |
+
"start": "0",
|
75 |
+
"_shape": [],
|
76 |
+
"dtype": "int64",
|
77 |
+
"_np_random": null
|
78 |
+
},
|
79 |
+
"n_envs": 1,
|
80 |
+
"n_steps": 1024,
|
81 |
+
"gamma": 0.999,
|
82 |
+
"gae_lambda": 0.98,
|
83 |
+
"ent_coef": 0.01,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 64,
|
87 |
+
"n_epochs": 4,
|
88 |
+
"clip_range": {
|
89 |
+
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "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"
|
91 |
+
},
|
92 |
+
"clip_range_vf": null,
|
93 |
+
"normalize_advantage": true,
|
94 |
+
"target_kl": null,
|
95 |
+
"lr_schedule": {
|
96 |
+
":type:": "<class 'function'>",
|
97 |
+
":serialized:": "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"
|
98 |
+
}
|
99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ae3ca34c9488588f7a5b65948d48c4183814ce055655b187895a5fc097f83a09
|
3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ca62ffef727c0a839df6d59459babacaf855dd88b4e64502e85fffac7d46e0bb
|
3 |
+
size 43762
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
|
3 |
+
size 864
|
ppo-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.1.0+cu121
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.23.5
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (183 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 214.4859161, "std_reward": 81.86146489389608, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-23T03:13:11.013567"}
|