A Succinct Summary of Reinforcement Learning

01/03/2023
by   Sanjeevan Ahilan, et al.
0

This document is a concise summary of many key results in single-agent reinforcement learning (RL). The intended audience are those who already have some familiarity with RL and are looking to review, reference and/or remind themselves of important ideas in the field.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/03/2022

Accelerate Reinforcement Learning with PID Controllers in the Pendulum Simulations

We propose a Proportional Integral Derivative (PID) controller-based coa...
research
04/04/2022

Reinforcement Learning Agents in Colonel Blotto

Models and games are simplified representations of the world. There are ...
research
03/31/2023

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

This paper presents a review of the field of reinforcement learning (RL)...
research
11/30/2022

Targets in Reinforcement Learning to solve Stackelberg Security Games

Reinforcement Learning (RL) algorithms have been successfully applied to...
research
11/23/2022

Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model

Reinforcement learning (RL) is currently used in various real-life appli...
research
06/16/2022

Reinforcement Learning in Macroeconomic Policy Design: A New Frontier?

Agent-based computational macroeconomics is a field with a rich academic...
research
11/25/2020

Distributed Reinforcement Learning is a Dataflow Problem

Researchers and practitioners in the field of reinforcement learning (RL...

Please sign up or login with your details

Forgot password? Click here to reset