Double action Q-learning for obstacle avoidance in a dynamically changing environment

In this paper, we propose a new method for solving the reinforcement learning problem in a dynamically changing environment, as in vehicle navigation, in which the Markov decision process used in traditional reinforcement learning is modified so that the response of the environment is taken into consideration for determining the agent's next state. This is achieved by changing the action-value function to handle three parameters at a time, namely, the current state, action taken by the agent, and action taken by the environment. As it considers the actions by the agent and environment, it is termed "double action". Based on the Q-learning method, the proposed method is implemented and the update rule is modified to handle all of the three parameters. Preliminary results show that the proposed method has the sum of rewards (negative) 89.5% less than that of the traditional method. Apart from that, our new method also has the total number of collisions and mean steps used in one episode 89.5% and 15.5% lower than that of the traditional method respectively.