Single Agent Reinforcement Learning

Single-agent reinforcement learning (RL) focuses on training a single agent to learn optimal actions within an environment to maximize cumulative rewards. Current research emphasizes improving sample efficiency, particularly through techniques like higher replay ratios and decoupling exploration and exploitation, often employing deep Q-learning or actor-critic methods. These advancements are significant because they enable more efficient training of RL agents for complex tasks, with applications ranging from robotics and autonomous vehicles to resource allocation in power grids and communication networks.