Agent Training

Agent training focuses on developing algorithms that enable multiple artificial agents to learn and cooperate effectively within complex environments. Current research emphasizes efficient training methods, including sparse training techniques to reduce computational costs, and the use of diverse algorithms like multi-agent reinforcement learning (MARL) and soft actor-critic (SAC) variants, often incorporating curriculum learning and population-based approaches. These advancements are crucial for improving the performance and scalability of multi-agent systems, with applications ranging from robotics and autonomous driving to resource management and large language model interaction.