Multi-agent Dual Level Reinforcement Learning of Strategy and Tactics in Competitive Games
Reinforcement learning has been used extensively to learn the low-level tactical choices during gameplay; however, less effort is invested in the strategic decisions governing the effective engagement of a diverse set of opponents.In this paper, a two-tier reinforcement learning model is created to play competitive games and effectively engage in m