This paper presents a comprehensive review of deep learning applications in the video
game industry, focusing on how these techniques can be utilized in game development …

Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …

Offline reinforcement learning leverages previously collected offline datasets to learn
optimal policies with no necessity to access the real environment. Such a paradigm is also …

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative
with humans without using human data. Although such agents can be obtained through self …

Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Population-based multi-agent reinforcement learning (PB-MARL) encompasses a range of
methods that merge dynamic population selection with multi-agent reinforcement learning …

Abstract We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent
environment simulates the target coverage control problems in the real world. MATE hosts …

Measuring and promoting policy diversity is critical for solving games with strong non-
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …

Recent algorithms have achieved superhuman performance at a number of two-player zero-
sum games such as poker and go. However, many real-world situations are multi-player …