Model-based reinforcement learning (RL) has shown great promise due to i...
Offline reinforcement learning (RL) allows agents to learn effective,
re...
Bilevel planning, in which a high-level search over an abstraction of an...
Effective and efficient planning in continuous state and action spaces i...
In realistic applications of object search, robots will need to locate t...
Recent advances in reinforcement learning (RL) have led to a growing int...
Despite recent, independent progress in model-based reinforcement learni...
Robotic planning problems in hybrid state and action spaces can be solve...
The problem of planning for a robot that operates in environments contai...
Real-world planning problems often involve hundreds or even thousands of...
Meta-planning, or learning to guide planning from experience, is a promi...
We present PDDLGym, a framework that automatically constructs OpenAI Gym...
We address the problem of efficient exploration for learning lifted oper...
We address the problem of effectively composing skills to solve sparse-r...
We address the problem of approximate model minimization for MDPs in whi...
Multi-object manipulation problems in continuous state and action spaces...
In many real-world robotic applications, an autonomous agent must act wi...
In many applications that involve processing high-dimensional data, it i...
In partially observed environments, it can be useful for a human to prov...