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 4626–4650 of 15113 papers

TitleStatusHype
Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement LearningCode0
Predicting Research Trends From ArxivCode0
On the Reliability and Generalizability of Brain-inspired Reinforcement Learning AlgorithmsCode0
XCS as a reinforcement learning approach to automatic test case prioritizationCode0
XCSF for Automatic Test Case PrioritizationCode0
XIRL: Cross-embodiment Inverse Reinforcement LearningCode0
Sequential memory improves sample and memory efficiency in Episodic ControlCode0
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion DemonstrationsCode0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov ModelCode0
On the Reuse Bias in Off-Policy Reinforcement LearningCode0
Low Emission Building Control with Zero-Shot Reinforcement LearningCode0
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global WorkspaceCode0
Neural Reward MachinesCode0
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence ArchitecturesCode0
Neural Sequence Model Training via α-divergence MinimizationCode0
Zero-Shot Reinforcement Learning via Function EncodersCode0
Neural SLAM: Learning to Explore with External MemoryCode0
Setting up a Reinforcement Learning Task with a Real-World RobotCode0
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement LearningCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision ProblemsCode0
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
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Benchmark Results

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