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 2991–3000 of 15113 papers

TitleStatusHype
A Hierarchical Model for Device Placement—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks—0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning—0
Deep Communicating Agents for Abstractive Summarization—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis—0
Show:102550
← PrevPage 300 of 1512Next →

Benchmark Results

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