Mobile Robot Path Planning Method Based on Deep Reinforcement Learning Algorithm

Path planning is an important part of the research field of mobile robots, and it is the premise for mobile robots to complete complex tasks. This paper proposes a reflective reward design method based on potential energy function, and combines the ideas of multi-agent and multi-task learning to form a new training framework. The reflective reward represents the quality of the agent’s current decision relative to the past historical decision sequence, using the second-order information of the historical reward sequence. The policy or value function update of the master agent is then assisted by the reflective agent. The method proposed in this paper can easily extend the existing deep reinforcement learning algorithm based on value function and policy gradient, and then form a new learning method, so that the agent has the reflective characteristics in human learning after making full use of the reward information. It is good at distinguishing the optimal action in the corresponding state. Experiments in pathfinding scenarios verify the effectiveness of the algorithm in sparse reward scenarios. Compared with other algorithms, the deep reinforcement learning algorithm has higher exploration success rate and stability. Experiments in survival scenarios verify the improvement effect of the reward feature enhancement method based on the auxiliary task learning mechanism on the original algorithm. Simulation experiments confirm the effectiveness of the proposed algorithm for solving the path planning problem of mobile robots in dynamic environments and the superiority of deep reinforcement learning algorithms. The simulation results show that the algorithm can accurately avoid unknown obstacles and reach the target point, and the planned path is the shortest and the energy consumed by the robot is the least. This demonstrates the effectiveness of deep reinforcement learning algorithms for local path planning and real-time decision making.