Control Benchmark

Control benchmarks are standardized tasks used to evaluate the performance of reinforcement learning (RL) algorithms in controlling simulated or real-world systems. Current research focuses on developing benchmarks that assess robustness to visual distractions, high-dimensional continuous control in complex robotics simulations, and sample efficiency in achieving near-optimal control, often employing techniques like distributional RL and model-based planning. These benchmarks are crucial for advancing RL algorithms, enabling objective comparisons of different approaches and ultimately driving progress in areas such as robotics, autonomous systems, and game playing.