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 1841–1850 of 15113 papers

TitleStatusHype
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems—0
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels—0
Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning ProgramsCode0
Beyond Human Preferences: Exploring Reinforcement Learning Trajectory Evaluation and Improvement through LLMs—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Operator World Models for Reinforcement LearningCode0
Multi-agent Cooperative Games Using Belief Map Assisted TrainingCode0
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning—0
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion—0
Efficient World Models with Context-Aware TokenizationCode2
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Benchmark Results

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