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 3851–3875 of 15113 papers

TitleStatusHype
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Assume-Guarantee Reinforcement Learning—0
Distributed Learning on Heterogeneous Resource-Constrained Devices—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
A Generative Framework for Simultaneous Machine Translation—0
Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control—0
Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT—0
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities—0
Deep RL with Hierarchical Action Exploration for Dialogue Generation—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise—0
A General Theory of Relativity in Reinforcement Learning—0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
Deep Sets for Generalization in RL—0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
Deep Surrogate Assisted Generation of Environments—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
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Benchmark Results

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