Upload PPO LunarLander-v2 trained agent
Browse files- Fixed_Lunar_Lander.zip +3 -0
- Fixed_Lunar_Lander/_stable_baselines3_version +1 -0
- Fixed_Lunar_Lander/data +99 -0
- Fixed_Lunar_Lander/policy.optimizer.pth +3 -0
- Fixed_Lunar_Lander/policy.pth +3 -0
- Fixed_Lunar_Lander/pytorch_variables.pth +3 -0
- Fixed_Lunar_Lander/system_info.txt +9 -0
- README.md +37 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
Fixed_Lunar_Lander.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c5e036747e9ad01fcb306f5aa639262cb5b73681413431d05609b2197562f952
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size 146265
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Fixed_Lunar_Lander/_stable_baselines3_version
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2.0.0a5
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Fixed_Lunar_Lander/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__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 ",
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7fae22a57400>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fae22a57490>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fae22a57520>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fae22a575b0>",
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"_build": "<function ActorCriticPolicy._build at 0x7fae22a57640>",
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"forward": "<function ActorCriticPolicy.forward at 0x7fae22a576d0>",
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fae22a57760>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fae22a577f0>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7fae22a57880>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fae22a57910>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fae22a579a0>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fae22a57a30>",
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"__abstractmethods__": "frozenset()",
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},
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"_shape": [],
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"dtype": "int64",
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|
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},
|
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"n_envs": 1,
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"lr_schedule": {
|
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":type:": "<class 'function'>",
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|
98 |
+
}
|
99 |
+
}
|
Fixed_Lunar_Lander/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6f9d34a9b3b0da1b0113d69d87254b718cc295036d78d17c2662d08420e55472
|
3 |
+
size 88057
|
Fixed_Lunar_Lander/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f2fb05d8acd44ae98d362a1ed8ac4bd17ad221b3040cedc048d81874ce6e31f7
|
3 |
+
size 43329
|
Fixed_Lunar_Lander/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
Fixed_Lunar_Lander/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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
|
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
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|
|
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: 258.11 +/- 54.37
|
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 0x7fae22a57400>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fae22a57490>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fae22a57520>", 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replay.mp4
ADDED
Binary file (176 kB). View file
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results.json
ADDED
@@ -0,0 +1 @@
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1 |
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{"mean_reward": 258.1064364, "std_reward": 54.37204765067936, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-11T17:53:51.935493"}
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