https://github.com/ikkihou/myrl
My personal Deep Reinforcement Learning implementations in Rust
https://github.com/ikkihou/myrl
reinforcement-learning-algorithms rust
Last synced: 3 months ago
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My personal Deep Reinforcement Learning implementations in Rust
- Host: GitHub
- URL: https://github.com/ikkihou/myrl
- Owner: ikkihou
- Created: 2025-04-10T03:19:01.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2025-04-10T03:28:16.000Z (3 months ago)
- Last Synced: 2025-04-10T04:31:47.692Z (3 months ago)
- Topics: reinforcement-learning-algorithms, rust
- Language: Rust
- Homepage:
- Size: 68.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MyRL
Welcome to **MyRL**, a Rust-based project for exploring reinforcement learning concepts and implementations.
## Features
- Written in Rust for performance and safety.
- Modular design for easy experimentation.
- Support for various RL algorithms.
## Getting Started
1. Clone the repository:
```bash
git clone https://github.com/yourusername/myrl.git
cd myrl
```
2. Build the project:
```bash
cargo build
```
3. Run examples:
```bash
cargo run --example example_name
```
## Dependencies
The project relies on the following dependencies:
- [rand](https://crates.io/crates/rand): For random number generation.
- [serde](https://crates.io/crates/serde): For serialization and deserialization.
- [ndarray](https://crates.io/crates/ndarray): For numerical computations.
- [thiserror](https://crates.io/crates/thiserror): For error handling.
Make sure to check the `Cargo.toml` file for the exact versions.
## TODOs
- Implement additional RL algorithms such as DDPG and PPO.
- Add unit tests for core modules.
- Improve documentation with detailed examples and use cases.
- Optimize performance for large-scale simulations.
- Create a benchmarking suite to compare algorithm performance.
- Add support for custom environments.
- Refactor code for better readability and maintainability.
- Write a tutorial for beginners to get started with the project.
- Explore integration with visualization tools for better insights.
- Publish the project on crates.io for wider accessibility.