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 3861–3870 of 15113 papers

TitleStatusHype
Deep RL with Hierarchical Action Exploration for Dialogue Generation—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
A General Theory of Relativity in Reinforcement Learning—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
Deep Sets for Generalization in RL—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
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Benchmark Results

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