Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

Despite many debates in the first half of the twentieth century, it is now largely a truism that
humans and other animals build models of their environments and use them for prediction …

Deep reinforcement learning agents have achieved state-of-the-art results by directly
maximising cumulative reward. However, environments contain a much wider variety of …

Temporal abstraction is key to scaling up learning and planning in reinforcement learning.
While planning with temporally extended actions is well understood, creating such …

We consider the problem of constructing abstract representations for planning in high-
dimensional, continuous environments. We assume an agent equipped with a collection of …

In this paper we introduce a new unsupervised reinforcement learning method for
discovering the set of intrinsic options available to an agent. This set is learned by …

Autonomously discovering temporally extended actions, or skills, is a longstanding goal of
hierarchical reinforcement learning. We propose a new algorithm that combines skill …

Recent work has reawakened interest in goal-directed or 'model-based'choice, where
decisions are based on prospective evaluation of potential action outcomes. Concurrently …

Tasks that require many sequential decisions or complex solutions are hard to solve using
conventional reinforcement learning algorithms. Based on the semi Markov decision …

Building systems that autonomously create temporal abstractions from data is a key
challenge in scaling learning and planning in reinforcement learning. One popular approach …