
Contents
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5.1 Introduction and overview 5.1 Introduction and overview
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5.2 Reinforcement learning in psychology 5.2 Reinforcement learning in psychology
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5.3 Reinforcement learning 5.3 Reinforcement learning
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5.3.1 RL problem 5.3.1 RL problem
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Markov decision process Markov decision process
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Behavioral policy Behavioral policy
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Optimal behavioral policy Optimal behavioral policy
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5.3.2 Temporal difference learning 5.3.2 Temporal difference learning
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Q-learning Q-learning
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Q-learning example Q-learning example
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5.3.3 Speeding up temporal difference learning 5.3.3 Speeding up temporal difference learning
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Eligibility traces Eligibility traces
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Model-based RL Model-based RL
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Hierarchical RL Hierarchical RL
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Partial observability and state factorization Partial observability and state factorization
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5.3.4 Behavioral strategies 5.3.4 Behavioral strategies
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5.3.5 Actor-critic approaches 5.3.5 Actor-critic approaches
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5.4 Policy gradients 5.4 Policy gradients
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5.4.1 Formalization of policy gradients 5.4.1 Formalization of policy gradients
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5.4.2 Gradient estimation techniques 5.4.2 Gradient estimation techniques
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5.4.3 A racing car example 5.4.3 A racing car example
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5.4.4 Conclusions and relations to cognition and behavior 5.4.4 Conclusions and relations to cognition and behavior
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5.5 Exercises 5.5 Exercises
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5 Behavior is Reward-oriented
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Published:January 2017
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Abstract
Delving further into development, adaptation, and learning, this chapter considers the potential of reward-oriented optimization of behavior. Reinforcement learning (RL) is motivated from the Rescorla–Wagner model in psychology and behaviorism. Next, a detailed introduction to RL in artificial systems is provided. It is shown when and how RL works, but also current shortcomings and challenges are discussed. In conclusion, the chapter emphasizes that behavioral optimization and reward-based behavioral adaptations can be well-accomplished with RL. However, to be able to solve more challenging planning problems and to enable flexible, goal-oriented behavior, hierarchically and modularly structured models about the environment are necessary. Such models then also enable the pursuance of abstract reasoning and of thoughts that are fully detached from the current environmental state. The challenge remains how such models may actually be learned and structured.
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