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

Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Deep neural networks coupled with fast simulation and improved computational speeds
have led to recent successes in the field of reinforcement learning (RL). However, most …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Reinforcement learning combined with neural networks has recently led to a wide range of
successes in learning policies in different domains. For robot manipulation, reinforcement …

This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves
the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter …

Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are represented …

Safe exploration is regarded as a key priority area for reinforcement learning research. With
separate reward and safety signals, it is natural to cast it as constrained reinforcement …