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 6251–6275 of 15113 papers

TitleStatusHype
Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games—0
Strategising template-guided needle placement for MR-targeted prostate biopsy—0
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks—0
Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning—0
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems—0
Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem—0
Streaming Linear System Identification with Reverse Experience Replay—0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation—0
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning—0
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
Strongly-polynomial time and validation analysis of policy gradient methods—0
Structural Credit Assignment in Neural Networks using Reinforcement Learning—0
Structural Credit Assignment with Coordinated Exploration—0
Structural Return Maximization for Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Structure-aware reinforcement learning for node-overload protection in mobile edge computing—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Structured Dialogue Policy with Graph Neural Networks—0
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
Structured World Belief for Reinforcement Learning in POMDP—0
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling—0
Structure in Deep Reinforcement Learning: A Survey and Open Problems—0
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Benchmark Results

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