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 4541–4550 of 15113 papers

TitleStatusHype
PaCo: Parameter-Compositional Multi-Task Reinforcement LearningCode1
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning—0
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
Model-based Lifelong Reinforcement Learning with Bayesian ExplorationCode0
MoCoDA: Model-based Counterfactual Data AugmentationCode1
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Safe Policy Improvement in Constrained Markov Decision Processes—0
Task Phasing: Automated Curriculum Learning from DemonstrationsCode0
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator ControlCode1
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Benchmark Results

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