A Review Of Uncertainty For Deep Reinforcement Learning

Abstract

Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area.

Stats

• citations45
• S2 citations—
• github stars0
• HF likes0
• heat score12.47
• arxiv keylockwood2022a

Related papers

• Estimating Risk And Uncertainty In Deep Reinforcement Learning (2019)0.00
• A Review Of Cooperative Multi-agent Deep Reinforcement Learning (2019)19.08
• Deep Multiagent Reinforcement Learning: Challenges And Directions (2021)0.00
• Uncertainty-based Out-of-distribution Detection In Deep Reinforcement Learning (2019)7.50
• Deep Reinforcement Learning For Multi-agent Systems: A Review Of Challenges, Solutions And Applications (2018)22.57
• Efficient Exploration With Double Uncertain Value Networks (2017)0.00
• Modeling The Effects Of Environmental And Perceptual Uncertainty Using Deterministic Reinforcement Learning Dynamics With Partial Observability (2021)9.59
• Risk-sensitive Bayesian Games For Multi-agent Reinforcement Learning Under Policy Uncertainty (2022)0.00