Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Abstract

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

Path Planning Algorithm for Dual-Arm Robot Based on Depth Deterministic Gradient Strategy Algorithm

“…It extends Markov chains and decision theory and is widely applied in reinforcement learning and optimization problems. Reinforcement learning methods can be categorized into value-based methods and policy-based methods [24]. Value-based methods use techniques like deep Q-networks (DQN) to approximate the optimal value function and derive the corresponding optimal policy from the approximated value function [25].…”

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

“…[15][16][17][18][19][20]. The AI technique that we employed here is Deep Reinforcement Learning (DRL), a promising approach in handling advanced control problems [21][22][23]. It comprises Reinforcement Learning (RL) and Deep Learning (DL), where RL learns to make the right decisions through taking the best-known actions then, based on the action taken, receives penalties or rewards as feedback.…”