Deep Reinforcement Learning Techniques in Diversified Domains: A Survey

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.

Automated summary

There have been significant advancements in deep learning and reinforcement learning techniques, particularly in the fusion of these two areas known as Deep Reinforcement Learning (DRL). Despite successes in various domains, challenges such as generalization, meeting multiple objectives, divergence, and learning robust policies still persist. Researchers are currently focusing on improving the recording of experiences and refining policies for future actions to address these challenges.