Stabilizing training in reinforcement learning (RL) requires addressing challenges like high variance, non-stationary data, and unstable updates. Three key strategies include using experience replay and target networks, applying policy optimization techniques, and carefully designing rewards and environments. These methods help manage the inherent instability of RL by controlling how the agent learns from interactions and updates its policies.
First, experience replay and target networks are foundational for stabilizing value-based methods like DQN. Experience replay stores past transitions in a buffer and randomly samples them during training, breaking correlations between consecutive experiences. For example, in a game-playing agent, consecutive frames are highly similar, which can lead to overfitting. By mixing old and new data, the agent learns more robustly. Target networks provide stable learning targets by decoupling the network used to predict Q-values from the one used to update them. In DQN, a separate target network is updated periodically (e.g., every 1,000 steps) instead of continuously, preventing abrupt changes in Q-value estimates. This reduces oscillations in training and helps the agent converge more reliably.
Second, policy optimization techniques like trust region methods (e.g., TRPO, PPO) and gradient clipping address instability in policy updates. Trust Region Policy Optimization (TRPO) limits policy updates by constraining the KL divergence between old and new policies, ensuring changes are incremental. Proximal Policy Optimization (PPO) simplifies this by clipping the policy update ratio, preventing large deviations. For example, PPO’s clipped objective ensures that the new policy doesn’t stray too far from the old one, which is critical in environments with sparse rewards. Gradient clipping caps the magnitude of gradients during backpropagation, which is especially useful in actor-critic architectures. For instance, if the critic’s value estimates become erratic, clipping gradients (e.g., at 0.5) prevents the actor’s policy from overshooting viable solutions.
Finally, reward shaping and environment design play a crucial role. Sparse or poorly scaled rewards can destabilize learning. For example, an agent learning to navigate a maze might receive a reward only upon reaching the goal, leading to ineffective exploration. By adding intermediate rewards (e.g., for moving closer to the goal), the agent gets clearer feedback. Curriculum learning gradually increases task difficulty, such as training a robot to walk by first teaching balance, then small steps. Environment randomization (e.g., varying physics parameters in simulation) also improves robustness. For instance, altering friction or object masses in training helps the agent adapt to real-world unpredictability, reducing overfitting to specific conditions. These design choices create a more stable learning trajectory.