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

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

We describe a framework for multitask deep reinforcement learning guided by policy
sketches. Sketches annotate tasks with sequences of named subtasks, providing information …

Deep reinforcement learning has achieved many impressive results in recent years.
However, tasks with sparse rewards or long horizons continue to pose significant …

Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each
new skill requires considerable real-world data collection and manual representation …

Solving complex, temporally-extended tasks is a long-standing problem in reinforcement
learning (RL). We hypothesize that one critical element of solving such problems is the …

As a step towards developing zero-shot task generalization capabilities in reinforcement
learning (RL), we introduce a new RL problem where the agent should learn to execute …

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 …