File size: 3,122 Bytes
6947f24 c90a6e8 0302446 c90a6e8 6947f24 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
library_name: stable-baselines3
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: -21.00 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **PongNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **PongNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Codes
Github repos(Give a star if found useful):
* https://github.com/hishamcse/DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Robo-Chess
Kaggle Notebook:
* https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-3-optuna-cartpole-pong-br-out
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env PongNoFrameskip-v4 -orga hishamcse -f logs/
python -m rl_zoo3.enjoy --algo ppo --env PongNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env PongNoFrameskip-v4 -orga hishamcse -f logs/
python -m rl_zoo3.enjoy --algo ppo --env PongNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env PongNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env PongNoFrameskip-v4 -f logs/ -orga hishamcse
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.4),
('ent_coef', 1.6077823351479547e-08),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('gae_lambda', 0.9342974216877361),
('gamma', 0.999),
('learning_rate', 0.009929843682975054),
('n_envs', 8),
('n_epochs', 9),
('n_steps', 128),
('n_timesteps', 5000000.0),
('normalize', False),
('policy', 'CnnPolicy'),
('policy_kwargs',
'dict(net_arch=[dict(pi=[64, 64], vf=[64, '
'64]),],activation_fn=nn.Tanh)'),
('vf_coef', 0.7945615838365445)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|