Learning When to Switch: Adaptive Policy Selection via Reinforcement Learning

Autonomous agents often require multiple strategies to solve complex tasks, but determining when to switch between strategies remains challenging. This research introduces a reinforcement learning technique to learn switching thresholds between two orthogonal navigation policies. Using maze navigation as a case study, this work demonstrates how an agent can dynamically transition between systematic exploration (coverage) and goal-directed pathfinding (convergence) to improve task performance. Unlike fixed-threshold approaches, the agent uses Q-learning to adapt switching behavior based on coverage percentage and distance to goal, requiring only minimal domain knowledge: maze dimensions and target location. The agent does not require prior knowledge of wall positions, optimal threshold values, or hand-crafted heuristics; instead, it discovers effective switching strategies dynamically during each run. The agent discretizes its state space into coverage and distance buckets, then adapts which coverage threshold (20-60%) to apply based on observed progress signals. Experiments across 240 test configurations (4 maze sizes from 16×16 to 128×128 × 10 unique mazes × 6 agent variants) demonstrate that adaptive threshold learning outperforms both single-strategy agents and fixed 40% threshold baselines. Results show 23-55% improvements in completion time, 83% reduction in runtime variance, and 71% improvement in worst-case scenarios. The learned switching behavior generalizes within each size class to unseen wall configurations. Performance gains scale with problem complexity: 23% improvement for 16×16 mazes, 34% for 32×32, and 55% for 64×64, demonstrating that as the space of possible maze structures grows, the value of adaptive policy selection over fixed heuristics increases proportionally.