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 3526–3550 of 15113 papers

TitleStatusHype
On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms—0
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions—0
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement LearningCode2
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks—0
Does Sparsity Help in Learning Misspecified Linear Bandits?—0
On-line reinforcement learning for optimization of real-life energy trading strategy—0
Planning with Sequence Models through Iterative Energy Minimization—0
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Robust Risk-Aware Option Hedging—0
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning—0
Inverse Reinforcement Learning without Reinforcement LearningCode1
Control of synaptic plasticity via the fusion of reinforcement learning and unsupervised learning in neural networks—0
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic EnvironmentsCode0
marl-jax: Multi-Agent Reinforcement Leaning FrameworkCode1
Optimal Transport for Offline Imitation LearningCode1
Learning to Operate in Open Worlds by Adapting Planning Models—0
Communication Load Balancing via Efficient Inverse Reinforcement Learning—0
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
Deep RL with Hierarchical Action Exploration for Dialogue Generation—0
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models—0
Beam Management Driven by Radio Environment Maps in O-RAN Architecture—0
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale—0
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Benchmark Results

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