Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

We study the sample complexity of model-based reinforcement learning (henceforth RL) in
general contextual decision processes that require strategic exploration to find a near …

In the era of big data, all types of data with increasing samples and high-dimensional
attributes are demonstrating their important roles in various fields, such as data mining …

A generally intelligent agent faces a dilemma: it requires a complex sensorimotor space to
be capable of solving a wide range of problems, but many tasks are only feasible given the …

A social recommendation system has attracted a lot of attention recently in the research
communities of information retrieval, machine learning, and data mining. Traditional social …

Fully actuated multifingered robotic hands are often expensive and fragile. Low-cost
underactuated hands are appealing but present challenges due to the lack of analytical …

Abstract Model-based approaches to reinforcement learning (MBRL) exhibit favorable
performance in practice, but their theoretical guarantees in large spaces are mostly …

In real-world applications of reinforcement learning, it is often challenging to obtain a state
representation that is parsimonious and satisfies the Markov property without prior …