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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4101–4125 of 15113 papers

TitleStatusHype
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Investigation of reinforcement learning for shape optimization of profile extrusion dies—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems—0
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning—0
On Reinforcement Learning for the Game of 2048Code1
Neighboring state-based RL Exploration—0
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence SummarizationCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Hyperparameters in Contextual RL are Highly SituationalCode0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling—0
Lifelong Reinforcement Learning with Modulating MasksCode0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Variational Quantum Soft Actor-Critic for Robotic Arm Control—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Inverse Reinforcement Learning for Text Summarization—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Quantum policy gradient algorithms—0
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Benchmark Results

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