https://github.com/ilan-theodoro/basic-reinforcement-learning-framework
This is a project developed and evaluated in the course of Reinforcement Learning at the State University of Campinas.
https://github.com/ilan-theodoro/basic-reinforcement-learning-framework
reinforcement-learning reinforcement-learning-algorithms
Last synced: 4 months ago
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This is a project developed and evaluated in the course of Reinforcement Learning at the State University of Campinas.
- Host: GitHub
- URL: https://github.com/ilan-theodoro/basic-reinforcement-learning-framework
- Owner: ilan-theodoro
- License: mit
- Created: 2023-11-17T00:53:43.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-03-04T05:41:26.000Z (over 1 year ago)
- Last Synced: 2025-01-16T20:22:10.202Z (5 months ago)
- Topics: reinforcement-learning, reinforcement-learning-algorithms
- Language: Jupyter Notebook
- Homepage:
- Size: 3.25 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Basic Reinforcement Learning Framework.
This is a project developed and evaluated in the course of Reinforcement Learning at the State University of Campinas.
[][tests]
[][pre-commit]
[][black]
[tests]: https://github.com/ilan-francisco/basic-reinforcement-learning-framework/actions?workflow=Tests
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black
## Features
- Furama's Gymnasium integration
- Monte-carlo Classic Control
- Q-Learning Classic Control
- Sarsa-lambda Classic Control
- DQN
## Requirements
This was tested with the following packages:
- torch==2.0.1
- gymnasium==0.29.1
- scikit-learn==1.3.2
- tqdm==4.66.1
- numba==0.58.1
- matplotlib==3.8.2
## Installation
You can install it by the following command (I did not have time to test it):
```console
$ pip3 install git+https://github.com/ilan-francisco/basic-reinforcement-learning-framework.git
```
## Usage
Here is an example of basic usage:
```python
import matplotlib.pyplot as plt
from rl_final_project.agent import Agent
from rl_final_project.dqn import DQNControl
from rl_final_project.dqn import DQNFunction
from rl_final_project.environment import EnvironmentNormalizer
env = EnvironmentNormalizer.from_gym("CartPole-v1")
n_states = env.observation_space.shape[0]
q_function = DQNFunction(
n_actions=env.action_space.n,
n_feat=n_states,
batch_size=128
)
agent = Agent(
q_function,
n_actions=env.action_space.n,
eps_greedy_function="dqn",
stochasticity_factor=0.0,
)
control = DQNControl(
lr=0.001,
tau=0.005,
env=env,
agent=agent,
num_episodes=1000,
gamma=0.99,
batch_size=128,
reward_mode="default"
)
eps_rewards = control.fit()
# plot the results
import matplotlib.pyplot as plt
plt.plot(eps_rewards)
plt.title("Monte-Carlo Control with DQN")
plt.xlabel("Episode")
plt.ylabel("Reward")
plt.show()
```
## Contributing
Contributions are very welcome.
To learn more, see the [Contributor Guide].
## License
Distributed under the terms of the [MIT license][license],
_Basic Reinforcement Learning Framework._ is free and open-source software.
## Issues
If you encounter any problems,
please [file an issue] along with a detailed description.
[license]: https://github.com/ilan-francisco/mo436_final_project/blob/main/LICENSE
[contributor guide]: https://github.com/ilan-francisco/mo436_final_project/blob/main/CONTRIBUTING.md
[command-line reference]: https://mo436_final_project.readthedocs.io/en/latest/usage.html