Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …

We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

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 …

Increasingly, algorithms are supplanting human decision-makers in pricing goods and
services. To analyze the possible consequences, we study experimentally the behavior of …

There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …

From the early days of computing, games have been important testbeds for studying how
well machines can do sophisticated decision making. In recent years, machine learning has …