Many human behavioral and brain imaging studies have used narratively structured stimuli
(eg, written, audio, or audiovisual stories) to better emulate real-world experience in the …

Animals frequently make decisions based on expectations of future reward (" values").
Values are updated by ongoing experience: places and choices that result in reward are …

To make reinforcement learning more sample efficient, we need better credit assignment
methods that measure an action's influence on future rewards. Building upon Hindsight …

Animals have a developed ability to explore that aids them in important tasks such as
locating food, exploring for shelter, and finding misplaced items. These exploration skills …

The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term …

Temporal Difference (TD) learning is ubiquitous in reinforcement learning, where it is often
combined with off-policy sampling and function approximation. Unfortunately learning with …

We address the problem of credit assignment in reinforcement learning and explore
fundamental questions regarding the way in which an agent can best use additional …

Optimal control of nonlinear systems by using adaptive dynamic programming (ADP)
methods is always a hot topic in recent years. However, unknown nonlinear systems with …

How do we formalize the challenge of credit assignment in reinforcement learning?
Common intuition would draw attention to reward sparsity as a key contributor to difficult …

Off-policy sampling and experience replay are key for improving sample efficiency and
scaling model-free temporal difference learning methods. When combined with function …