Reinforcement Learning Benchmark

Reinforcement learning (RL) benchmarks are standardized environments designed to evaluate the performance and generalization capabilities of RL algorithms. Current research focuses on developing benchmarks that address challenges like sample efficiency, safety, generalization to unseen environments (including visual generalization), and robustness to adversarial attacks, often employing diverse model architectures such as recurrent neural networks and linear policy networks alongside various algorithms including PPO and evolution strategies. These benchmarks are crucial for advancing RL research by providing a common ground for comparing algorithms and identifying areas needing improvement, ultimately accelerating progress towards more robust and efficient RL agents for real-world applications in robotics, autonomous driving, and resource allocation.