# Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization

## TL;DR

This paper introduces a genetic algorithm-based method to optimize parameters in deep reinforcement learning, specifically for DDPG with HER, resulting in faster and better performance in robotic manipulation tasks.

## Contribution

It presents a novel approach using genetic algorithms to optimize RL parameters, improving learning speed and effectiveness in complex robotic tasks.

## Key findings

- Faster learning compared to original algorithms
- Improved task performance in robotic manipulation
- Effective parameter optimization using GA

## Abstract

Reinforcement learning (RL) enables agents to take decision based on a reward function. However, in the process of learning, the choice of values for learning algorithm parameters can significantly impact the overall learning process. In this paper, we use a genetic algorithm (GA) to find the values of parameters used in Deep Deterministic Policy Gradient (DDPG) combined with Hindsight Experience Replay (HER), to help speed up the learning agent. We used this method on fetch-reach, slide, push, pick and place, and door opening in robotic manipulation tasks. Our experimental evaluation shows that our method leads to better performance, faster than the original algorithm.