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 5201–5225 of 15113 papers

TitleStatusHype
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents—0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning—0
Representation Learning in Deep RL via Discrete Information Bottleneck—0
Representation Learning in Low-rank Slate-based Recommender Systems—0
Representation Learning on Graphs: A Reinforcement Learning Application—0
Representation Matters: Offline Pretraining for Sequential Decision Making—0
Representations for Stable Off-Policy Reinforcement Learning—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents—0
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework—0
RESEARCH ARTICLE A Reinforcement Learning Model of Joy, Distress, Hope and Fear—0
—0
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States—0
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention—0
Residential Demand Response Applications Using Batch Reinforcement Learning—0
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty—0
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation—0
Residual Policy Learning for Powertrain Control—0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—0
Residual Reinforcement Learning for Robot Control—0
Residual Reinforcement Learning from Demonstrations—0
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments—0
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting—0
Resilient Constrained Reinforcement Learning—0
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations—0
Show:102550
← PrevPage 209 of 605Next →

Benchmark Results

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