MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
PositiveArtificial Intelligence
- A new framework called Multi-Objective Multi-Agent Actor-Critic (MOMA-AC) has been introduced to address gaps in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). This framework utilizes Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) algorithms, featuring a multi-headed actor network and a centralized critic to optimize trade-off policies across conflicting objectives in continuous environments.
- The development of MOMA-AC is significant as it enhances the capabilities of reinforcement learning in multi-agent settings, potentially leading to more efficient and effective solutions in complex environments. This advancement could have far-reaching implications for AI applications in various fields, including robotics and autonomous systems.
— via World Pulse Now AI Editorial System