According to cognitive psychology and related disciplines, the development of complex
problem-solving behaviour in biological agents depends on hierarchical cognitive …

Abstract We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the
context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors-from …

Value functions are a core component of reinforcement learning. The main idea is to to
construct a single function approximator V (s; theta) that estimates the long-term reward from …

One of the key challenges of artificial intelligence is to learn models that are effective in the
context of planning. In this document we introduce the predictron architecture. The …

Understanding how an artificial agent may represent, acquire, update, and use large
amounts of knowledge has long been an important research challenge in artificial …

Importance sampling (IS) is a common reweighting strategy for off-policy prediction in
reinforcement learning. While it is consistent and unbiased, it can result in high variance …

Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally
challenging. Existing approaches have utilized goal relabeling on collected experiences to …

State construction is important for learning in partially observable environments. A general
purpose strategy for state construction is to learn the state update using a Recurrent Neural …

An agent learning an option in hierarchical reinforcement learning must solve three
problems: identify the option's subgoal (termination condition), learn a policy, and learn …

Cognitive Psychology and related disciplines have identified several critical mechanisms
that enable intelligent biological agents to learn to solve complex problems. There exists …