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 2381–2390 of 15113 papers

TitleStatusHype
CARoL: Context-aware Adaptation for Robot Learning—0
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine—0
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control—0
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control—0
CodeContests+: High-Quality Test Case Generation for Competitive Programming—0
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement LearningCode0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
Dissecting Long Reasoning Models: An Empirical StudyCode0
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models—0
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Benchmark Results

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