Paper Info
Title
Evo-RL: evolutionary-driven reinforcement learning
Abstract
ABSTRACTIn this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing Evo-RL to be adaptive to different environments. In addition, Evo-RL facilitates learning on environments with reward-less states, which makes it more suited for real-world problems with incomplete information. To show that Evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within Evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our Evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget.
Year
DOI
Venue
2021
10.1145/3449726.3459475
Genetic and Evolutionary Computation Conference
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Ahmed Hallawa112.12
Thorsten Born200.34
Anke Schmeink327746.57
Guido Dartmann410219.53
Arne Peine502.03
Lukas Martin601.35
Giovanni Iacca760135.11
Gusz Eiben8261.37
Gerd Ascheid91205144.76