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 2841–2850 of 15113 papers

TitleStatusHype
Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping—0
Evaluation-Time Policy Switching for Offline Reinforcement Learning—0
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Learning to reset in target search problemsCode0
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning—0
Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers—0
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches—0
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey—0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
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Benchmark Results

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