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

Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of
artificial intelligence during the last several years. Recent works have focused on deep …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

Competition and collaboration form complex interaction patterns between the agents and
objects involved. Only by understanding these interaction patterns, we can reveal the …

Autonomous navigation has recently gained great interest in the field of reinforcement
learning. However, little attention was given to the time-optimal velocity control problem, ie …

In the last decade, there have been significant advances in multi-agent reinforcement
learning (MARL) but there are still numerous challenges, such as high sample complexity …

Pommerman is a multi-agent environment that has received considerable attention from
researchers in recent years. This environment is an ideal benchmark for multi-agent training …

Two-Player alternate-turn perfect-information zero-sum games have been suggested as a
testbed for Artificial Intelligence research since Shannon in 1950s. In this thesis, we …

In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to
outperform human grandmasters in games such as Chess, Shogi and Go with little to no …