Adaptive Learning Rate Strategies in Deep Reinforcement Learning Agents

Abstract

The evolution of deep reinforcement learning (DRL) has revolutionized the capacity of artificial agents to make intelligent decisions in dynamic environments. However, the success of DRL models is heavily dependent on hyperparameter tuning, particularly the learning rate. An improperly selected learning rate can lead to poor convergence, instability, or suboptimal policy learning. This study investigates adaptive learning rate strategies to enhance the training efficiency and performance stability of DRL agents. Unlike static learning rate schedules, adaptive techniques dynamically modify the learning rate during training based on agent performance, loss gradient trends, or environment feedback. This manuscript explores four adaptive strategies: AdaGrad, RMSprop, Adam, and Cyclical Learning Rates, within the context of deep Q-networks (DQN) and proximal policy optimization (PPO) agents across two simulation environments—CartPole and LunarLander. Simulation-based analysis evaluates cumulative rewards, convergence epochs, and stability metrics under different learning rate paradigms.

The results suggest that adaptive methods like Adam and Cyclical Learning Rates outperform static settings in terms of faster convergence and policy robustness. Statistical analysis with ANOVA reveals significant variance in performance metrics among strategies, validating the efficacy of adaptive learning rate integration. A comparative table summarizes the statistical and empirical findings. The study concludes that incorporating intelligent learning rate adaptation mechanisms in DRL architectures can significantly optimize agent learning processes without manual hyperparameter tuning. Future implications include real-time adaptive strategies that respond to evolving task complexities in robotics and autonomous systems.