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Deep Reinforcement Learning Techniques in Diversified Domains: A Survey

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Abstract

There have been tremendous improvements in deep learning and reinforcement learning techniques. Automating learning and intelligence to the full extent remains a challenge. The amalgamation of Reinforcement Learning and Deep Learning has brought breakthroughs in games and robotics in the past decade. Deep Reinforcement Learning (DRL) involves training the agent with raw input and learning via interaction with the environment. Motivated by recent successes of DRL, we have explored its adaptability to different domains and application areas. This paper also presents a comprehensive survey of the work done in recent years and simulation tools used for DRL. The current focus of researchers is on recording the experience in a better way, and refining the policy for futuristic moves. It is found that even after obtaining good results in Atari, Go, Robotics, multi-agent scenarios, there are challenges such as generalization, satisfying multiple objectives, divergence, learning robust policy. Furthermore, the complex environment and multiple agents are throwing new challenges, which is an open area of research.

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  1. Computer Science, Bennett University, Plot No 8-11, TechZone, Street 2, Greater Noida, Uttar Pradesh, 201310, India

    Surbhi Gupta, Gaurav Singal & Deepak Garg

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Idea for the article: SG, GS, DG. Literature search: SG. Initial draft Prepared by: SG. Critical revisions done by: GS, DG. Contribution in writing future scope and challenges: GS, DG, SG.

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Appendix

Appendix

Table 5 defines key MDP parameters that were considered in diversified applications. This table gives insight into the state, and action that were considered in the literature. This table also signifies that many problems can be solved by considering binary reward function.

Table 5 DRL key MDP parameters for diversified applications
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Gupta, S., Singal, G. & Garg, D. Deep Reinforcement Learning Techniques in Diversified Domains: A Survey. Arch Computat Methods Eng 28, 4715–4754 (2021). https://doi.org/10.1007/s11831-021-09552-3

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