In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by
maximizing an exogenous reward signal while interacting with its environment. In real world …

Training Reinforcement Learning (RL) agents in high-stakes applications might be too
prohibitive due to the risk associated to exploration. Thus, the agent can only use data …

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control
policies for high dimensional systems, but its relatively poor sample complexity often …

The principle of optimism in the face of uncertainty is prevalent throughout sequential
decision making problems such as multi-armed bandits and reinforcement learning (RL). To …

Achieving robust performance is crucial when applying deep reinforcement learning (RL) in
safety critical systems. Some of the state of the art approaches try to address the problem …

Recent advances in the reinforcement learning (RL) literature have enabled roboticists to
automatically train complex policies in simulated environments. However, due to the poor …

A major component of overfitting in model-free reinforcement learning (RL) involves the case
where the agent may mistakenly correlate reward with certain spurious features from the …

Traditional robotic approaches rely on an accurate model of the environment, a detailed
description of how to perform the task, and a robust perception system to keep track of the …

Many continuous control tasks have easily formulated objectives, yet using them directly as
a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many …

Model-based reinforcement learning (RL) is considered to be a promising approach to
reduce the sample complexity that hinders model-free RL. However, the theoretical …