An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Knowledge trajectory of eSports as an emerging field of research
Purpose Research on electric sports (eSports) has experienced significant growth in recent
years as a consequence of increasing connectivity, institutionalization, and technological …
years as a consequence of increasing connectivity, institutionalization, and technological …
Iteratively learn diverse strategies with state distance information
In complex reinforcement learning (RL) problems, policies with similar rewards may have
substantially different behaviors. It remains a fundamental challenge to optimize rewards …
substantially different behaviors. It remains a fundamental challenge to optimize rewards …
Continuously discovering novel strategies via reward-switching policy optimization
We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse
strategies in complex RL environments by iteratively finding novel policies that are both …
strategies in complex RL environments by iteratively finding novel policies that are both …
The application of metaverse in healthcare
Y Wang, M Zhu, X Chen, R Liu, J Ge, Y Song… - Frontiers in Public …, 2024 - frontiersin.org
While metaverse is widely discussed, comprehension of its intricacies remains limited to a
select few. Conceptually akin to a three-dimensional embodiment of the Internet, the …
select few. Conceptually akin to a three-dimensional embodiment of the Internet, the …
Transformer in reinforcement learning for decision-making: a survey
Reinforcement learning (RL) has become a dominant decision-making paradigm and has
achieved notable success in many real-world applications. Notably, deep neural networks …
achieved notable success in many real-world applications. Notably, deep neural networks …
Tools for landscape analysis of optimisation problems in procedural content generation for games
Abstract The term Procedural Content Generation (PCG) refers to the (semi-) automatic
generation of game content by algorithmic means, and its methods are becoming …
generation of game content by algorithmic means, and its methods are becoming …
The graph structure of two-player games
In this paper, we analyse two-player games by their response graphs. The response graph
has nodes which are strategy profiles, with an arc between profiles if they differ in the …
has nodes which are strategy profiles, with an arc between profiles if they differ in the …
Research on opponent modeling framework for multi-agent game confrontation
J Luo, W Zhang, W Yuan, Z Hu… - Journal of …, 2022 - dc-china-simulation …
As the key technology of multi-agent game confrontation, opponent modeling is a typical
cognitive modeling method of agent's behavior. Several typical models of multi-agent game …
cognitive modeling method of agent's behavior. Several typical models of multi-agent game …
Evaluating strategy exploration in empirical game-theoretic analysis
In empirical game-theoretic analysis (EGTA), game models are extended iteratively through
a process of generating new strategies based on learning from experience with prior …
a process of generating new strategies based on learning from experience with prior …