Behavior Cloning-Enhanced Deep Reinforcement Learning for Robot Navigation Via Dynamic Reward and Adaptive Replanning

Abstract

Deep reinforcement learning (DRL) is a core technology for mobile robot navigation in diverse environments, yet existing Behavior Cloning (BC)-enhanced DRL methods suffer two critical challenges: fixed imitation constraints suppress autonomous exploration in late training stages despite stabilizing early learning, and goal-obstacle avoidance task conflicts impede robust action selection during navigation. To address these issues, this paper proposes an Adaptive Strategy Deep Reinforcement Learning (ADRL) method, which reformulates BC as a progressively released transitional constraint and builds a stage-aware transition framework for robot navigation. Specifically, ADRL dynamically fuses Twin Delayed Deep Deterministic Policy Gradient (TD3) with BC via a value-driven imitation scheduling mechanism, which adaptively modulates the expert-online data mixing ratio and BC regularization strength based on critic feedback to accelerate convergence and realize a smooth shift from imitation-dominant to exploration-driven learning. A phase-aligned dynamic weight composite reward function is designed, which embeds motion constraints and stage-aware priority adjustment to mitigate reward sparsity and align learning objectives with policy maturity. Additionally, a lightweight adaptive replanning mechanism is developed as an evaluation stabilizer, which generates obstacle-avoiding waypoints by obstacle density when the robot stagnates, resolving goal-obstacle avoidance conflicts without al