Reinforcement Learning and Model-based Planning in Practice: A Survey of Algorithmic Rationale and Domain Applications

Reinforcement Learning (RL) is a foundational framework in Artificial Intelligence (AI) that enables agents to acquire optimal decision-making strategies through interactions with their environments. Building on principles of trial-and-error learning, RL adapts dynamically by leveraging feedback in the form of rewards or penalties. This paper provides a comprehensive survey of RL, its integration with Deep Learning into Deep Reinforcement Learning (DRL), and the emerging field of model-based pla

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ethics.ai (14 August 2026), “Reinforcement Learning and Model-based Planning in Practice: A Survey of Algorithmic Rationale and Domain Applications,” evidence record 19545, https://ethics.ai/record/19545 (originally published by Artificial Intelligence Review).

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