Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

A generally intelligent agent faces a dilemma: it requires a complex sensorimotor space to
be capable of solving a wide range of problems, but many tasks are only feasible given the …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

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

We study the problem of representation learning in goal-conditioned hierarchical
reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks …

The reinforcement learning paradigm is a popular way to address problems that have only
limited environmental feedback, rather than correctly labeled examples, as is common in …

This paper addresses the problem of planning under uncertainty in large Markov Decision
Processes (MDPs). Factored MDPs represent a complex state space using state variables …

Learning models of the environment from data is often viewed as an essential component to
building intelligent reinforcement learning (RL) agents. The common practice is to separate …

Many stochastic planning problems can be represented using Markov Decision Processes
(MDPs). A difficulty with using these MDP representations is that the common algorithms for …

The combinatorial explosion that plagues planning and reinforcement learning (RL)
algorithms can be moderated using state abstraction. Prohibitively large task …