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

In this paper, we characterize the behavior of canonical value-based deep reinforcement
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …

In the future, powerful AI systems may be deployed in high-stakes settings, where a single
failure could be catastrophic. One technique for improving AI safety in high-stakes settings is …

The advent of AlphaGo and its successors marked the beginning of a new paradigm in
playing games using artificial intelligence. This was achieved by combining Monte Carlo …

We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …

Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems from game playing and robotics have been …

In this paper we investigate the use of reinforcement-learning based prediction approaches
for a real drinking-water treatment plant. Developing such a prediction system is a critical …

Standardized lossy video coding is at the core of almost all real-world video processing
pipelines. Rate control is used to enable standard codecs to adapt to different network …

Resource-constrained hardware such as edge devices or cell phones often rely on cloud
servers to provide the required computational resources for inference in deep vision models …

Solving complex planning problems has been a long-standing challenge in computer
science. Learning-based subgoal search methods have shown promise in tackling these …