Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
This work addresses a key bottleneck in making curiosity-based RL methods more accessible and applicable by reducing the need for extensive hyperparameter tuning.
The paper tackles the challenge of hyperparameter sensitivity in curiosity-based exploration for reinforcement learning, proposing Hyper to mitigate this issue and demonstrating its robust performance across various environments.
The exploration \& exploitation dilemma poses significant challenges in reinforcement learning (RL). Recently, curiosity-based exploration methods achieved great success in tackling hard-exploration problems. However, they necessitate extensive hyperparameter tuning on different environments, which heavily limits the applicability and accessibility of this line of methods. In this paper, we characterize this problem via analysis of the agent behavior, concluding the fundamental difficulty of choosing a proper hyperparameter. We then identify the difficulty and the instability of the optimization when the agent learns with curiosity. We propose our method, hyperparameter robust exploration (\textbf{Hyper}), which extensively mitigates the problem by effectively regularizing the visitation of the exploration and decoupling the exploitation to ensure stable training. We theoretically justify that \textbf{Hyper} is provably efficient under function approximation setting and empirically demonstrate its appealing performance and robustness in various environments.