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 6001–6025 of 15113 papers

TitleStatusHype
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
You Only Live Once: Single-Life Reinforcement Learning—0
Model Predictive Control via On-Policy Imitation Learning—0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog SystemsCode0
Boosting Offline Reinforcement Learning via Data Rebalancing—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
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals—0
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning—0
Revisiting the Roles of "Text" in Text Games—0
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale—0
Reinforcement Learning for ConnectX—0
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 Multilevel Reinforcement Learning Framework for PDE-based ControlCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
Query Rewriting for Effective Misinformation Discovery—0
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement LearningCode0
A Scalable Finite Difference Method for Deep Reinforcement Learning—0
Adaptive patch foraging in deep reinforcement learning agents—0
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic LocomotionCode0
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects—0
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
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Benchmark Results

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