Deep Reinforcement Learning: A Survey

🤖 Plain-English Summary

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.

🔑 Key Findings

• 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.
• 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.
• Researchers have proposed various solutions and new theories to solve these problems and promote the development of DRL.

🔗 Related Resources