Exploration in Reinforcement Learning
Summary
Exploration in reinforcement learning is a critical aspect of developing effective learning algorithms that can navigate complex environments and make optimal decisions. The concept of “exploration potential” has been introduced as a measure of how thoroughly an agent has explored its environment, taking into account the reward structure of the problem. This approach provides a criterion for achieving asymptotic optimality across an entire environment class. Meanwhile, efforts to improve the interpretability of deep Q-networks have led to the development of architectures that offer global explanations of model behavior through key-value memories, attention mechanisms, and reconstructible embeddings. These interpretable models can achieve comparable performance to state-of-the-art deep Q-learning algorithms while providing insights into the features extracted by the neural networks. However, challenges remain in preventing overfitting to training trajectories and ensuring robust performance on out-of-sample examples.