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 9026–9050 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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