Bo Li

Exploration and Exploitation: A Study on Sample Efficiency in Reinforcement Learning With Multifaceted Curiosity Rewards and Adaptive Experience Replay Utilisation in Sparse Reward Environments

A reinforcement learning framework built upon the Soft Actor Critic architecture, which integrates multifaceted curiosity rewards (MCR) and adaptive experience replay utilisation (AERU) (MCR‐AERU SAC), demonstrating superior sample efficiency and excellent robustness in large‐scale sparse reward environments.