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About 7750 Improving Exploration in Evolution Strategies For Deep Reinforcement
This document proposes hybridizing evolution strategies (ES) with novelty search (NS) and quality diversity (QD) algorithms to improve directed exploration for reinforcement learning tasks. ES is a black-box optimization method that can efficiently train deep neural networks on RL problems but lacks directed exploration abilities. NS and QD were originally developed for neuroevolution and encourage exploration of novel behaviors through populations of agents. The paper experiments with combining NS, NSR, and NSRA algorithms with ES to avoid local optima on sparse and deceptive RL tasks like simulated robot locomotion and Atari games. Results show the hybrid algorithms achieve higher performance than standard ES.
Detailed Information
| Author: | ['Oromion'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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