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 761–770 of 15113 papers

TitleStatusHype
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches—0
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse RewardsCode2
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning—0
Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers—0
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning—0
Learning to reset in target search problemsCode0
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question AnsweringCode3
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of DiscussionsCode0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
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Benchmark Results

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