This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

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 many sequential decision-making problems, the goal is to optimize a utility function while
satisfying a set of constraints on different utilities. This learning problem is formalized …

Safety in reinforcement learning has become increasingly important in recent years. Yet,
existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to …

This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP).
Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the …

Deep reinforcement learning (DRL) is currently used to solve Markov decision process
problems for which the environment is typically assumed to be stationary. In this paper, we …

Policy gradient (PG) algorithms are among the best candidates for the much-anticipated
applications of reinforcement learning to real-world control tasks, such as robotics. However …

This paper investigates conservative exploration in reinforcement learning where the
performance of the learning agent is guaranteed to be above a certain threshold throughout …

Many problems, such as online ad display, can be formulated as online bipartite matching.
The crucial challenge lies in the nature of sequentially-revealed online item information …

One of the challenges in online reinforcement learning (RL) is that the agent needs to trade
off the exploration of the environment and the exploitation of the samples to optimize its …