Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …

Intelligent agents need to select long sequences of actions to solve complex tasks. While
humans easily break down tasks into subgoals and reach them through millions of muscle …

Trading off exploration and exploitation in an unknown environment is key to maximising
expected return during learning. A Bayes-optimal policy, which does so optimally, conditions …

Exploration is critical to a reinforcement learning agent's performance in its given
environment. Prior exploration methods are often based on using heuristic auxiliary …

Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …

This letter presents latent sampling-based motion planning (L-SBMP), a methodology
toward computing motion plans for complex robotic systems by learning a plannable latent …

One of the key capabilities of intelligent agents is the ability to discover useful skills without
external supervision. However, the current unsupervised skill discovery methods are often …

Reward-free reinforcement learning (RL) is a framework which is suitable for both the batch
RL setting and the setting where there are many reward functions of interest. During the …