Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

02/27/2021
by   Michael Walton, et al.
0

In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we provide complete documentation of hyperparameters and source code required to reproduce these experiments easily and exactly.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2022

MultiRoboLearn: An open-source Framework for Multi-robot Deep Reinforcement Learning

It is well known that it is difficult to have a reliable and robust fram...
research
07/14/2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Existing evaluation suites for multi-agent reinforcement learning (MARL)...
research
08/26/2019

OpenSpiel: A Framework for Reinforcement Learning in Games

OpenSpiel is a collection of environments and algorithms for research in...
research
06/14/2020

Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms

Multi-agent deep reinforcement learning (MARL) suffers from a lack of co...
research
03/27/2021

KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning

Recently, deep Reinforcement Learning (RL) algorithms have achieved dram...
research
06/30/2020

Evaluating the Performance of Reinforcement Learning Algorithms

Performance evaluations are critical for quantifying algorithmic advance...
research
01/20/2022

Safety-Aware Multi-Agent Apprenticeship Learning

Our objective of this project is to make the extension based on the tech...

Please sign up or login with your details

Forgot password? Click here to reset