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 411–420 of 15113 papers

TitleStatusHype
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric VehiclesCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
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Benchmark Results

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