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

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Showing 6776–6800 of 15113 papers

TitleStatusHype
Effective ML Model Versioning in Edge Networks—0
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement—0
Effective reinforcement learning based local search for the maximum k-plex problem—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Effective Scheduling Function Design in SDN through Deep Reinforcement Learning—0
Effective sketching methods for value function approximation—0
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention—0
Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning—0
Effects of Conservatism on Offline Learning—0
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation—0
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned—0
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change—0
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion—0
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search—0
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation—0
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning—0
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning—0
Efficient collective swimming by harnessing vortices through deep reinforcement learning—0
Efficient Competitive Self-Play Policy Optimization—0
Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
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Benchmark Results

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