Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

In reinforcement learning (RL), an agent interacts with the environment by taking actions and
observing the next state and reward. When sampled probabilistically, these state transitions …

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to
maximize the accumulated reward, it often learns to exploit loopholes and misspecifications …

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 …

Although ground robotic autonomy has gained widespread usage in structured and
controlled environments, autonomy in unknown and off-road terrain remains a difficult …

Autonomous agents must often deal with conflicting requirements, such as completing tasks
using the least amount of time/energy, learning multiple tasks, or dealing with multiple …

In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision
problems with the objective of maximizing the long-term reward as well as satisfying …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Most reinforcement learning (RL) algorithms assume online access to the environment, in
which one may readily interleave updates to the policy with experience collection using that …