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 9001–9025 of 15113 papers

TitleStatusHype
Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Reinforcement Learning for Flexibility Design Problems—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachCode0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
A Survey on Deep Reinforcement Learning for Audio-Based Applications—0
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning—0
When Is Generalizable Reinforcement Learning Tractable?—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Multi-Agent Trust Region LearningCode1
Batch Reinforcement Learning Through Continuation Method—0
Hierarchical Meta Reinforcement Learning for Multi-Task EnvironmentsCode0
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Learning to Observe with Reinforcement Learning—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING—0
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
A Reduction Approach to Constrained Reinforcement Learning—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning—0
Exploring Transferability of Perturbations in Deep Reinforcement Learning—0
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Benchmark Results

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