In Multi-agent Reinforcement Learning (MARL), accurately perceiving
opponents' strategies is essential for both cooperative and adversarial
contexts, particularly within dynamic environments. While Proximal Policy
Optimization (PPO) and related algorithms such as Actor-Critic with Experience
Replay (ACER), Trust Region Policy Optimization (TRPO), and Deep Deterministic
Policy Gradient (DDPG) perform well in single-agent, stationary environments,
they suffer from high variance in MARL due to non-stationary and hidden
policies of opponents, leading to diminished reward performance. Additionally,
existing methods in MARL face significant challenges, including the need for
inter-agent communication, reliance on explicit reward information, high
computational demands, and sampling inefficiencies. These issues render them
less effective in continuous environments where opponents may abruptly change
their policies without prior notice. Against this background, we present
OPS-DeMo (Online Policy Switch-Detection Model), an online algorithm that
employs dynamic error decay to detect changes in opponents' policies. OPS-DeMo
continuously updates its beliefs using an Assumed Opponent Policy (AOP) Bank
and selects corresponding responses from a pre-trained Response Policy Bank.
Each response policy is trained against consistently strategizing opponents,
reducing training uncertainty and enabling the effective use of algorithms like
PPO in multi-agent environments. Comparative assessments show that our approach
outperforms PPO-trained models in dynamic scenarios like the Predator-Prey
setting, providing greater robustness to sudden policy shifts and enabling more
informed decision-making through precise opponent policy insights.

在多智能体强化学习中，准确地感知对手策略对于合作和对抗环境都是必不可少的，本文提出了一种在线算法 OPS-DeMo，通过使用动态误差衰减来检测对手策略的变化，并在多智能体环境中将 PPO 算法更有效地应用于对策略的训练，相较于 PPO，在动态情景下表现更出色，提供更强的鲁棒性和更准确的对手策略洞察。