Deep Reinforcement Learning: A Survey

Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In addition to value-based and policy-based DRL algorithms, the advances in maximum entropy-based DRL are summarized.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and making optimal or near-optimal decisions.
• 2 However, there are still many challenging problems in the theory and applications of DRL, especially in learning control tasks with limited samples, sparse rewards, and multiple agents.
• 3 Researchers have proposed various solutions and new theories to solve these problems and promote the development of DRL.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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