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 3071–3080 of 15113 papers

TitleStatusHype
Insurance pricing on price comparison websites via reinforcement learning—0
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse—0
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads—0
Omega-Regular Reward Machines—0
Neural Categorical Priors for Physics-Based Character Control—0
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs—0
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Benchmark Results

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