Reinforcement Learning Benchmarks
Summary
Reinforcement Learning Benchmarks are crucial tools for evaluating and comparing the performance of various RL algorithms. Notable examples include OpenAI Gym, a toolkit that offers a standardized interface for a diverse set of RL problems, and the MineRL Competition, which focuses on sample-efficient RL using human priors. These benchmarks provide researchers with common environments and metrics to assess their algorithms, facilitating progress in the field. They often feature complex, hierarchical, and sparse environments that challenge state-of-the-art techniques. By offering shared platforms and competitions, these benchmarks encourage collaboration, innovation, and the development of more efficient and effective RL algorithms, particularly in areas such as sample efficiency and the integration of human demonstrations with reinforcement learning.