DeepAI
Log In Sign Up

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

07/14/2021

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

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

OpenSpiel: A Framework for Reinforcement Learning in Games

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

Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms

Multi-agent deep reinforcement learning (MARL) suffers from a lack of co...
08/15/2020

Chrome Dino Run using Reinforcement Learning

Reinforcement Learning is one of the most advanced set of algorithms kno...
03/27/2021

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

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

Evaluating the Performance of Reinforcement Learning Algorithms

Performance evaluations are critical for quantifying algorithmic advance...
05/14/2021

RC2020 Report: Learning De-biased Representations with Biased Representations

As part of the ML Reproducibility Challenge 2020, we investigated the IC...