Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly
complex solutions to the problem. This increase in complexity often comes at the expense of …

Reasoning at multiple levels of temporal abstraction is one of the key attributes of
intelligence. In reinforcement learning, this is often modeled through temporally extended …

Exploration, consolidation and planning depend on the generation of sequential state
representations. However, these algorithms require disparate forms of sampling dynamics …

Covering option discovery has been developed to improve the exploration of RL in single-
agent scenarios with sparse reward signals, through connecting the most distant states in …

Temporal abstraction in reinforcement learning (RL), offers the promise of improving
generalization and knowledge transfer in complex environments, by propagating information …

We introduce a new skill-discovery algorithm that builds a discrete graph representation of
large continuous MDPs, where nodes correspond to skill subgoals and the edges to skill …

In goal-conditioned Hierarchical Reinforcement Learning (HRL), a high-level policy
periodically sets subgoals for a low-level policy, and the low-level policy is trained to reach …

Selecting exploratory actions that generate a rich stream of experience for better learning is
a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem …

While many option discovery methods have been proposed to accelerate exploration in
reinforcement learning, they are often heuristic. Recently, covering options was proposed to …

Abstraction can improve the sample efficiency of reinforcement learning. However, the
process of abstraction inherently discards information, potentially compromising an agent's …