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 4576–4600 of 15113 papers

TitleStatusHype
Boosting Offline Reinforcement Learning via Data Rebalancing—0
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog SystemsCode0
Teacher Forcing Recovers Reward Functions for Text GenerationCode1
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
You Only Live Once: Single-Life Reinforcement Learning—0
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals—0
Entropy Regularized Reinforcement Learning with Cascading Networks—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning—0
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning—0
Revisiting the Roles of "Text" in Text Games—0
A Multilevel Reinforcement Learning Framework for PDE-based ControlCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
DyFEn: Agent-Based Fee Setting in Payment Channel Networks—0
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems—0
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale—0
Reinforcement Learning for ConnectX—0
When to Update Your Model: Constrained Model-based Reinforcement LearningCode1
Multi-trainer Interactive Reinforcement Learning System—0
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based EnvironmentsCode1
Skill-Based Reinforcement Learning with Intrinsic Reward MatchingCode1
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
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Benchmark Results

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