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An introduction to reinforcement learning

This paper provides an introduction to the field of deep reinforcement learning (deep RL), which combines reinforcement learning (RL) techniques with deep learning. The main points covered in the paper are:

  1. Motivation: Deep RL has become increasingly popular due to its success in addressing challenging sequential decision-making problems, such as playing Atari games, mastering Go, and beating professionals in Poker. It also has potential for real-world applications in robotics, self-driving cars, finance, and smart grids.

  2. Machine learning and deep learning: The paper provides an overview of the three main types of machine learning tasks: supervised learning, unsupervised learning, and reinforcement learning. It also introduces the concept of deep learning, which has led to dramatic improvements in learning from high-dimensional data.

  3. Reinforcement learning framework: The paper presents the general RL framework and the Markov Decision Process (MDP) setting. It discusses different methodologies for training deep RL agents, including model-free approaches (learning a value function and/or a policy) and model-based approaches (planning algorithms that use a learned model of the environment).

  4. Generalization in RL: The paper highlights the importance of feature selection, function approximator selection, modifying the objective function, and hierarchical learning for generalization in RL.

  5. Challenges and best practices: The paper discusses the main challenges of using RL in the online setting, such as the exploration-exploitation dilemma and the use of a replay memory. It also presents existing benchmarks for evaluating RL algorithms and best practices for ensuring consistency and reproducibility of results.

  6. General settings and applications: The paper covers more general settings than MDPs, including Partially Observable Markov Decision Processes (POMDPs), distribution of MDPs, learning without explicit reward functions, and multi-agent systems. It also discusses applications of deep RL in various domains and the relationship between deep RL and neuroscience.

The goal of this introduction is to guide the reader towards effective use and understanding of core deep RL methods, provide references for further reading, and enable the reader to apply deep RL algorithms and pursue further research in the field.

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