Corpus ID: 235125753

Learning to make consumption-saving decisions in a changing environment: an AI approach

  title={Learning to make consumption-saving decisions in a changing environment: an AI approach},
  author={Rui Shi},
This exercise offers an innovative learning mechanism to model economic agent’s decision-making process using a deep reinforcement learning algorithm. In particular, this AI agent has limited or no information on the underlying economic structure and its own preference. I model how the AI agent learns in terms of how it collects and processes information. It is able to learn in real time through constantly interacting with the environment and adjusting its actions accordingly. I illustrate that… Expand


Reinforcement Learning in Economics and Finance
A state-of-the-art of reinforcement learning techniques are proposed, and applications in economics, game theory, operation research and finance are presented. Expand
Deep Reinforcement Learning in a Monetary Model
We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamicExpand
Reinforcement Learning: An Introduction
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications. Expand
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
A brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art is considered and the survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems. Expand
Continuous control with deep reinforcement learning
This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs. Expand
Concepts in Bounded Rationality: Perspectives from Reinforcement Learning
of “Concepts in Bounded Rationality: Perspectives from Reinforcement Learning”, by David Abel, A.M., Brown University, May 2019. In this thesis, I explore the relevance of computational reinforcementExpand
A Brief Survey of Deep Reinforcement Learning
This survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic, and highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. Expand
The Impact of Machine Learning on Economics
An assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions, and some highlights from the emerging econometric literature combining machine learning and causal inference. Expand
Artificial Intelligence: A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
Will Artificial Intelligence Replace Computational Economists Any Time Soon?
Artificial intelligence (AI) has impressive applications in many fields (speech recognition, computer vision, etc.). This paper demonstrates that AI can be also used to analyze complex andExpand