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 …
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …
becoming key to precision medicine—from identifying disease risks and taking preventive …
Deep counterfactual regret minimization
Abstract Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large
imperfect-information games. It converges to an equilibrium by iteratively traversing the …
imperfect-information games. It converges to an equilibrium by iteratively traversing the …
Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge
A Singla, S Padakandla… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
This paper presents our method for enabling a UAV quadrotor, equipped with a monocular
camera, to autonomously avoid collisions with obstacles in unstructured and unknown …
camera, to autonomously avoid collisions with obstacles in unstructured and unknown …
Actor-critic policy optimization in partially observable multiagent environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …
challenging problem in artificial intelligence. Among the most common approaches are …
Recent advances in deep reinforcement learning applications for solving partially observable markov decision processes (pomdp) problems: Part 1—fundamentals …
The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …
Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games
We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …
applies to extensive-form settings with any number of players, including mechanism design …
Dream: Deep regret minimization with advantage baselines and model-free learning
We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies
in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash …
in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash …
Remix: Regret minimization for monotonic value function factorization in multiagent reinforcement learning
Value function factorization methods have become a dominant approach for cooperative
multiagent reinforcement learning under a centralized training and decentralized execution …
multiagent reinforcement learning under a centralized training and decentralized execution …
Double neural counterfactual regret minimization
Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for
solving Imperfect Information Games (IIG). However, the original CRF algorithm only works …
solving Imperfect Information Games (IIG). However, the original CRF algorithm only works …