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

Showing 12611270 of 15113 papers

TitleStatusHype
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radarsCode1
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Benchmark Results

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