| 开发人员: | Justin Daludado (2) | ||
| 价格: | 免费 | ||
| 排名: | 0 | ||
| 评价: | 0 我要评价 | ||
| 列表: | 0 + 0 | ||
| 积分: | 0 + 0 ¡ | ||
| App Store | |||
说明
Built with MLX, ExploreRL provides an interactive sandbox to test various environments and algorithms with different hyperparameters to observe how RL agents learn through trial and error.
LEARN BY DOING
Train agents on classic adapted environments and observe learning in real time. Watch values grow, track reward curves, and develop intuition for how different algorithms and hyperparameters affect performance. Read and learn about reinforcement learning concepts in the documentation.
ALGORITHMS
Four common implementations:
- Q-Learning
- SARSA
- Deep Q-Network (DQN) for discrete action spaces
- Soft Actor-Critic (SAC) for continuous control
INCLUDED ENVIRONMENTS
Adapted from classic standards:
Toy Text: Frozen Lake, Blackjack, Taxi, Cliff Walking
Classic Control: CartPole, Mountain Car, Mountain Car Continuous, Acrobot, Pendulum
Box2D: Lunar Lander, Lunar Lander Continuous, Car Racing, Car Racing Discrete
FEATURES
- Environment-specific settings
- Real-time training metrics and reward visualization
- Configurable hyperparameters: learning rate, discount factor, epsilon decay, batch size
- Agent library with save, load, duplicate, and export functionality
- Evaluation mode to test trained agents without further training
- Educational documentation covering RL concepts, neural networks, and algorithms
- Custom seed for reproducibility
Note: Training performance will vary by device. Devices with newer hardware and more memory will have a better experience.
屏幕截图
新版变化
- 版本: 1.0.1
- 已更新:
- - fixed an issue where files were incorrectly exported
- fix performance issues in some environments
- updated list designs
价格
- 今天: 免费
- 最小值: 免费
- 最大值: 免费
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- Justin Daludado
- 平台: iOS 应用 (2)
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其他信息
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