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 8401–8450 of 15113 papers

TitleStatusHype
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Positive-Unlabeled Reward Learning—0
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning—0
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
Posterior Sampling for Large Scale Reinforcement Learning—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
Post-processing Networks: A Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning—0
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles—0
Potential Field Guided Actor-Critic Reinforcement Learning—0
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach—0
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions—0
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach—0
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches—0
Power and accountability in reinforcement learning applications to environmental policy—0
Power and Accountability in RL-driven Environmental Policy—0
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach—0
Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning—0
Power Grid Cascading Failure Mitigation by Reinforcement Learning—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks—0
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration—0
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning—0
Practical Kernel-Based Reinforcement Learning—0
Practical Marginalized Importance Sampling with the Successor Representation—0
Practical Risk Measures in Reinforcement Learning—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis—0
PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning—0
Predecessor Features—0
Predicted Variables in Programming—0
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning—0
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation—0
Predicting Multiple Actions for Stochastic Continuous Control—0
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning—0
Prediction Based Decision Making for Autonomous Highway Driving—0
Prediction Improves Simultaneous Neural Machine Translation—0
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation—0
Predictive auxiliary objectives in deep RL mimic learning in the brain—0
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards—0
Predictive Control Using Learned State Space Models via Rolling Horizon Evolution—0
Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning—0
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach—0
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning—0
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Benchmark Results

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