Learning to perceive and act by trial and error

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Neuronlike adaptive elements that can solve difficult learning control problems

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Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming

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Dynamic Programming: Deterministic and Stochastic Models

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Neural Networks for Control

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Reactive reasoning and planning

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Networks and the best approximation property

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Neural Networks for Control

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Pengi: an implementation of a theory of activity

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Temporal credit assignment in reinforcement learning

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Universal plans for reactive robots in unpredictable environments

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Learning and Sequential Decision Making

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The Dynamic Structure of Everyday Life

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Learning Sequential Decision Rules Using Simulation Models and Competition

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Classifier Systems and the Animat Problem

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Intelligent behavior as an adaptation to the task environment

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Landmark learning: An illustration of associative search

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Universal planning: an (almost) universally bad idea

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Reference frames for animate vision

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Learning and Problem Solving with Multilayer Connectionist Systems

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Situated control rules

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Introduction to stochastic dynamic programming

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Penguins can make cake

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First Results with Dyna, an Integrated Architecture for Learning, Planning and Reacting

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Sequential Decision Problems and Neural Networks

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Representation and Automatic Synthesis of Reaction Plans

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INCREMENTAL LEARNING OF CONTROL STRATEGIES WITH GENETIC ALGORITHMS

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Refinement of robot motor skills through reinforcement learning

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Explaining temporal differences to create useful concepts for evaluating states

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A Role for Anticipation in Reactive Systems that Learn

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ON BECOMING REACTIVE

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Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment

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Explanations of Empirically Derived Reactive Plans

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A Self-organizing Associative Memory System for Control Applications

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A Formal Framework for Learning in Embedded Systems

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Some experiments in adaptive state-space robotics

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Visual Routines: Where Bottom-Up and Top-Down Processing Meet

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Reactive behavior, learning, and anticipation

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Self-improving reactive agents based on reinforcement learning, planning and teaching

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Deictic codes for the embodiment of cognition

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Reinforcement learning for robots using neural networks

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Memory Representations in Natural Tasks

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Dyna, an Integrated Architecture for Learning, Planning, and Reacting

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Learning to Perceive and Act

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Active Perception and Reinforcement Learning

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Reinforcement learning with perceptual aliasing: the perceptual distinctions approach

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Perceptual Aliasing in JCMB (or, Where on Earth is IPAB?)

Harrison, M. (2008)

Perception control

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