Decision-Making in Reinforcement Learning

TL;DR

This paper compares probabilistic and deterministic decision-making strategies in deep reinforcement learning, demonstrating that probabilistic approaches like Bayesian dropout outperform deterministic methods in uncertain environments.

Contribution

It provides a comparative analysis of decision-making strategies in deep reinforcement learning, highlighting the effectiveness of Bayesian dropout in uncertain scenarios.

Findings

Bayesian dropout outperforms other strategies in uncertain environments.

Probabilistic approaches are more effective in the long run.

Exploration strategies significantly impact learning performance.

Abstract

In this research work, probabilistic decision-making approaches are studied, e.g. Bayesian and Boltzmann strategies, along with various deterministic exploration strategies, e.g. greedy, epsilon-Greedy and random approaches. In this research work, a comparative study has been done between probabilistic and deterministic decision-making approaches, the experiments are performed in OpenAI gym environment, solving Cart Pole problem. This research work discusses about the Bayesian approach to decision-making in deep reinforcement learning, and about dropout, how it can reduce the computational cost. All the exploration approaches are compared. It also discusses about the importance of exploration in deep reinforcement learning, and how improving exploration strategies may help in science and technology. This research work shows how probabilistic decision-making approaches are better in the long run as compared to the deterministic approaches. When there is uncertainty, Bayesian dropout approach proved to be better than all other approaches in this research work.