NevarokML: UNevarokMLBaseAlgorithm
The UNevarokMLBaseAlgorithm class in NevarokML represents a base algorithm for reinforcement learning. It provides a foundation for using various reinforcement learning algorithms, such as PPO, A2C, DDPG, DQN, SAC, and TD3.
Overview
The UNevarokMLBaseAlgorithm class serves as a base class for different reinforcement learning algorithms in NevarokML. It provides common functionality and settings that are shared across these algorithms.
Reinforcement learning algorithms enable agents to learn and make decisions based on interaction with an environment. They optimize policies or value functions to maximize rewards or achieve specific goals. The NevarokML plugin offers several algorithms that can be used to train agents in various environments.
The UNevarokMLBaseAlgorithm class provides factory methods to create instances of specific algorithms with predefined settings.
API Reference
For detailed information on the API of the UNevarokMLBaseAlgorithm class, refer to the following documentation pages:
Conclusion
The UNevarokMLBaseAlgorithm class forms the backbone of reinforcement learning algorithms in NevarokML. It provides a common interface and shared functionality for implementing different algorithms. By utilizing this class, you can easily configure and customize your reinforcement learning experiments.