Debugging reinforcement learning (RL) models involves systematically identifying and resolving issues that prevent the agent from learning effectively. Start by verifying core components like the reward function, environment interactions, and policy updates. For example, if the agent isn’t improving, check whether rewards are correctly calculated and passed to the agent. A common mistake is misaligning reward signals with the intended goal—like rewarding the wrong action or scaling rewards improperly. Tools like TensorBoard or custom logging can help visualize rewards over time to spot anomalies. Additionally, test the environment separately to ensure it responds correctly to actions. If the environment has a bug (e.g., incorrect state transitions), the agent can’t learn valid behavior.
Next, analyze exploration versus exploitation dynamics. RL agents often rely on strategies like epsilon-greedy or entropy regularization to balance trying new actions versus sticking to known good ones. If the agent gets stuck in suboptimal behavior, adjust exploration parameters. For instance, increasing the exploration rate (epsilon) in a Q-learning agent might help it discover better policies. Similarly, monitor the action distribution: if the agent’s actions lack diversity, it might be exploiting too early. Tools like action histograms or policy entropy plots can reveal this. For example, in a grid-world navigation task, if the agent always moves left despite obstacles, it might need more exploration or a reward adjustment.
Finally, inspect hyperparameters and training stability. RL algorithms are sensitive to settings like learning rates, discount factors, and batch sizes. A learning rate that’s too high can cause unstable updates, while one that’s too low slows learning. Use techniques like gradient clipping or adaptive optimizers (e.g., Adam) to stabilize training. For example, in policy gradient methods, large gradient updates can destabilize the policy—clipping gradients to a maximum value mitigates this. Also, validate the discount factor (gamma): if it’s too low, the agent might ignore long-term rewards. Test hyperparameters in controlled ablation studies to isolate their impact. If training plateaus, consider adjusting the network architecture or adding reward shaping to guide the agent.