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 10251–10275 of 15113 papers

TitleStatusHype
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions—0
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Edge-Compatible Reinforcement Learning for Recommendations—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
Generalization through Diversity: Improving Unsupervised Environment Design—0
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization—0
Effective Medical Test Suggestions Using Deep Reinforcement Learning—0
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
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Benchmark Results

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