Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning

Abstract

It is well known that quantifying uncertainty in the action-value estimates is crucial for efficient exploration in reinforcement learning. Ensemble sampling offers a relatively computationally tractable way of doing this using randomized value functions. However, it still requires a huge amount of computational resources for complex problems. In this paper, we present an alternative, computationally efficient way to induce exploration using index sampling. We use an indexed value function to represent uncertainty in our action-value estimates. We first present an algorithm to learn parameterized indexed value function through a distributional version of temporal difference in a tabular setting and prove its regret bound. Then, in a computational point of view, we propose a dual-network architecture, Parameterized Indexed Networks (PINs), comprising one mean network and one uncertainty network to learn the indexed value function. Finally, we show the efficacy of PINs through computational experiments.

arXiv comment17 pages, 4 figures, Proceedings of the 34th AAAI Conference on Artificial Intelligence

References in corpus (8)

• Analysis of Thompson Sampling for the multi-armed bandit problem
• Noisy Networks for Exploration
• Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
• The Uncertainty Bellman Equation and Exploration
• Behaviour Suite for Reinforcement Learning
• Deep Reinforcement Learning with a Natural Language Action Space
• How much does your data exploration overfit? Controlling bias via information usage
• Randomized Value Functions via Multiplicative Normalizing Flows