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 1526–1550 of 15113 papers

TitleStatusHype
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional CurriculumCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Demonstration-Guided Reinforcement Learning with Learned SkillsCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
Gated Hierarchical Attention for Image CaptioningCode1
De novo PROTAC design using graph-based deep generative modelsCode1
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise RolloutsCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Modeling 3D Shapes by Reinforcement LearningCode1
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual ExplanationsCode1
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement LearningCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Model Primitive Hierarchical Lifelong Reinforcement LearningCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
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Benchmark Results

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