Searching with Opponent-Awareness
arXiv:2104.10508
Abstract
We propose Searching with Opponent-Awareness (SOA), an approach to leverage opponent-aware planning without explicit or a priori opponent models for improving performance and social welfare in multi-agent systems. To this end, we develop an opponent-aware MCTS scheme using multi-armed bandits based on Learning with Opponent-Learning Awareness (LOLA) and compare its effectiveness with other bandits, including UCB1. Our evaluations include several different settings and show the benefits of SOA are especially evident with increasing number of agents.
References in corpus (5)
- Playing Atari with Deep Reinforcement Learning
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
- Maintaining cooperation in complex social dilemmas using deep reinforcement learning