SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 5001–5025 of 15113 papers

TitleStatusHype
A Survey of Deep Reinforcement Learning in Video Games—0
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions—0
A Survey of Demonstration Learning—0
A Survey of Explainable Reinforcement Learning—0
A Survey of Exploration Methods in Reinforcement Learning—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
A Survey of In-Context Reinforcement Learning—0
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress—0
A Survey of Knowledge-based Sequential Decision Making under Uncertainty—0
A Survey of Meta-Reinforcement Learning—0
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
A review of motion planning algorithms for intelligent robotics—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Survey of Reinforcement Learning from Human Feedback—0
A Survey of Reinforcement Learning Informed by Natural Language—0
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions—0
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
A Survey on Causal Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified