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

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

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 …

Through many recent successes in simulation, model-free reinforcement learning has
emerged as a promising approach to solving continuous control robotic tasks. The research …

Visual navigation in unknown environments poses significant challenges due to the
presence of many obstacles and low-texture scenes. These factors may cause frequent …

Autonomously discovering temporally extended actions, or skills, is a longstanding goal of
hierarchical reinforcement learning. We propose a new algorithm that combines skill …

Representation learning is a central challenge across a range of machine learning areas. In
reinforcement learning, effective and functional representations have the potential to …

We reformulate the option framework as two parallel augmented MDPs. Under this novel
formulation, all policy optimization algorithms can be used off the shelf to learn intra-option …

Building systems that autonomously create temporal abstractions from data is a key
challenge in scaling learning and planning in reinforcement learning. One popular approach …

Reinforcement learning can train policies that effectively perform complex tasks. However for
long-horizon tasks, the performance of these methods degrades with horizon, often …