Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
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 …
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Many real-world applications require artificial agents to compete and coordinate with other
agents in complex environments. As a stepping stone to this goal, the domain of StarCraft …
agents in complex environments. As a stepping stone to this goal, the domain of StarCraft …
Collaborating with humans without human data
Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …
A survey and critique of multiagent deep reinforcement learning
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 …
led to a dramatic increase in the number of applications and methods. Recent works have …
Nash learning from human feedback
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm
for aligning large language models (LLMs) with human preferences. Typically, RLHF …
for aligning large language models (LLMs) with human preferences. Typically, RLHF …
A unified game-theoretic approach to multiagent reinforcement learning
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 …
partly to the recent success of deep neural networks. The simplest form of MARL is …
Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …
processing problems and now have an increasingly important role in scientific discovery. A …