Commit
•
1381896
1
Parent(s):
c960a30
Upload PPO LunarLander-v2 trained agent
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: 252.46 +/- 21.14
|
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 0x7effe08a7520>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7effe08a75b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7effe08a7640>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7effe08a76d0>", "_build": "<function ActorCriticPolicy._build at 0x7effe08a7760>", "forward": "<function ActorCriticPolicy.forward at 0x7effe08a77f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7effe08a7880>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7effe08a7910>", "_predict": "<function ActorCriticPolicy._predict at 0x7effe08a79a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7effe08a7a30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7effe08a7ac0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7effe08a7b50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7effe08b0b80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000001, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1687456636175823180, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAALNmOr3hUqs5+btBPRLYU77GtuC5uMCEPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044699955300053773, "_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": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "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 sha256:8f20812ee256d63257436027d136f951671125cc3c3fe913b4a0506a95fb5b51
|
3 |
+
size 146581
|
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 0x7effe08a7520>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7effe08a75b0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7effe08a7640>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7effe08a76d0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7effe08a7760>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7effe08a77f0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7effe08a7880>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7effe08a7910>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7effe08a79a0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7effe08a7a30>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7effe08a7ac0>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7effe08a7b50>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7effe08b0b80>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 1000448,
|
25 |
+
"_total_timesteps": 1000001,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": null,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1687456636175823180,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAALNmOr3hUqs5+btBPRLYU77GtuC5uMCEPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044699955300053773,
|
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": "Generator(PCG64)"
|
69 |
+
},
|
70 |
+
"action_space": {
|
71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "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",
|
73 |
+
"n": "4",
|
74 |
+
"start": "0",
|
75 |
+
"_shape": [],
|
76 |
+
"dtype": "int64",
|
77 |
+
"_np_random": "Generator(PCG64)"
|
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 sha256:5fba6f0638330d749c6d9978b158fea2f25745554ae97cc2d5fe8bf49d9fd1b1
|
3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:93c4b6fb8ae6f8eacc6db2e4ad220189b9ae48695b5b5f1bc82b345ca3ead357
|
3 |
+
size 43329
|
ppo-LunarLander-v2/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-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.0.1+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (195 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 252.4606041027085, "std_reward": 21.144879035872343, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-22T18:34:56.710720"}
|