Name
Title | ||
|---|---|---|
Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance. |
Abstract | ||
|---|---|---|
Reinforcement learning (RL) problems often feature deceptive local optima, and methods that optimize purely for reward often fail to learn strategies for overcoming them [2]. Deep neuroevolution and novelty search have been proposed as effective alternatives to gradient-based methods for learning RL policies directly from pixels. We introduce and evaluate the use of novelty search over agent action sequences by Levenshtein distance as a means for promoting innovation. We also introduce a method for stagnation detection and population regeneration inspired by recent developments in the RL community [5], [1] that is derived from novelty search. Our methods extend a state-of-the-art method for deep neuroevolution using a simple genetic algorithm (GA) designed to efficiently learn deep RL policy network weights [6]. Results provide further evidence that GAs are competitive with gradient-based algorithms for deep RL in the Atari 2600 benchmark. Results also demonstrate that novelty search over agent action sequences can be effectively used as a secondary source of evolutionary selection pressure.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3321956 | GECCO |
Keywords | DocType | Volume |
deep reinforcement learning, genetic algorithms, novelty search | Conference | abs/1902.03142 |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 1 | 0.36 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ethan C. Jackson | 1 | 1 | 0.36 |
| Mark Daley | 2 | 166 | 22.18 |