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 1376–1400 of 15113 papers

TitleStatusHype
A Reinforcement Learning Environment for Mathematical Reasoning via Program SynthesisCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic RewardsCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Know Your Action Set: Learning Action Relations for Reinforcement LearningCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Active Reinforcement Learning for Robust Building ControlCode1
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning FrameworkCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Cryptocurrency Portfolio Management with Deep Reinforcement LearningCode1
LaND: Learning to Navigate from DisengagementsCode1
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
BabyAI 1.1Code1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
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Benchmark Results

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