Value Network

Value networks are crucial components in reinforcement learning (RL), particularly for assigning credit in complex sequential tasks and improving the efficiency of policy learning. Current research focuses on addressing limitations of value networks, such as inaccurate reward prediction and susceptibility to local optima, through methods like Monte Carlo estimation (bypassing large networks entirely), Mixture-of-Experts architectures for improved scalability, and value function search to refine approximations. These advancements aim to enhance the performance and sample efficiency of RL agents across diverse applications, from game playing and robotics to chemical synthesis planning.