Accelerated Q-learning for fail state and action spaces

Accelerated Q-learning algorithm is proposed for environment having both goal and fail states. It extends Q-learning, a well-known scheme in reinforcement learning. Unlike this conventional Q-learning, the proposed algorithm keeps track of the past failure experiences as a separate fail state-action value, Q_F. Agent uses this value along with a goal state-action value, Q_N, which is calculated and updated using conventional Q-learning, to modify the exploratory behavior during learning phase. Effectiveness of the proposed accelerated Q-learning algorithm is verified in a grid world environment. The proposed algorithm significantly reduces a convergence speed to find out the optimal path from start state to goal state while maximizing its receiving rewards.