Deep reinforcement learning: Difference between revisions

Revision as of 04:52, 5 May 2025

Revision as of 05:39, 6 May 2025

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'''Deep reinforcement learning''' ('''DRL''') is a subfield of [[machine learning]] that combines principles of [[reinforcement learning]] (RL) and [[deep learning]]. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using [[Artificial neural networks|deep neural networks]] to represent policies, value functions, or environment models. This integration enables DRL systems to process high-dimensional inputs, such as images or continuous control signals, making the approach effective for solving complex tasks. Since the introduction of the [[Q-learning|deep Q-network (DQN)]] in 2015, DRL has achieved significant successes across domains including [[Video game|games]], [[robotics]], and [[Autonomous system|autonomous systems]], and is increasingly applied in areas such as healthcare, finance, and autonomous vehicles.

== Deep reinforcement learning ==