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A Comprehensive Survey of Multiagent Reinforcement Learning
TLDR
The benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied, and an outlook for the field is provided.
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Reinforcement Learning and Dynamic Programming Using Function Approximators
TLDR
Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP, with a focus on continuous-variable problems.
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Multi-agent Reinforcement Learning: An Overview
TLDR
This chapter reviews a representative selection of multi-agent reinforcement learning algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competitive) tasks.
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A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
TLDR
The workings of the natural gradient is described, which has made its way into many actor-critic algorithms over the past few years, and a review of several standard and natural actor-Critic algorithms is given.
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A comprehensive survey of multi-agent reinforcement learning
TLDR
The benefits and challenges of MARL are described along with some of the problem domains where MARL techniques have been applied, and an outlook for the field is provided.
Online least-squares policy iteration for reinforcement learning control
TLDR
An online PI algorithm that evaluates policies with the so-called least-squares temporal difference for Q-functions (LSTD-Q) is proposed, which is found to work well for a wide range of its parameters, and to learn successfully in a real-time example.
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Evolutionary function approximation for reinforcement learning
TLDR
The techniques proposed in this article concern just evolutionary optimization, without involving RL, and the reasons for the oscillation of the performance curves of online NEAT and onlineNEAT+Q are explained.
Experience Replay for Real-Time Reinforcement Learning Control
TLDR
This paper evaluates ER RL on real-time control experiments that involve a pendulum swing-up problem and the vision-based control of a goalkeeper robot, and develops a general ER framework that can be combined with essentially any incremental RL technique, and instantiate this framework for the approximate Q-learning and SARSA algorithms.
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Efficient Model Learning Methods for Actor–Critic Control
TLDR
Two new actor-critic algorithms for reinforcement learning that learn a process model and a reference model which represents a desired behavior are proposed, from which desired control actions can be calculated using the inverse of the learned process model.
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Vision and Control for UAVs: A Survey of General Methods and of Inexpensive Platforms for Infrastructure Inspection
TLDR
This report surveys vision and control methods that can be applied to low-cost UAVs, and lists some popular inexpensive platforms and application fields where they are useful.
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