This paper analyzes reinforcement learning (RL) algorithms for Markov decision processes
(MDPs) under the average-reward criterion. We focus on Q-learning algorithms based on …

Discovering useful temporal abstractions, in the form of options, is widely thought to be key
to applying reinforcement learning and planning to increasingly complex domains. Building …

The average-reward formulation is a natural and important formulation of learning and
planning problems, yet has received much less attention than the episodic and discounted …

Skill acquisition is among the most remarkable aspects of human intelligence. It involves
discovering purposeful behavioural modules, retaining them as skills, honing them through …

Planning and goal-conditioned reinforcement learning aim to create more efficient and
scalable methods for complex, long-horizon tasks. These approaches break tasks into …