Fully cooperative games with state and input constraints using reinforcement learning based on control barrier functions
Shihan Liu,
Lijun Liu,
Zhen Yu
Abstract:This paper provides a novel safe reinforcement learning (RL) control algorithm to solve safe optimal problems for fully cooperative (FC) games of discrete‐time multiplayer nonlinear systems with state and input constraints. The FC game is a special case of nonzero‐sum (NZS) games, where all players cooperate to accomplish a common task. The algorithm is proposed based on the policy iteration (PI) framework utilizing only the measured data along the system trajectories in the environment. Different from most wo… Show more
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