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 11351–11375 of 15113 papers

TitleStatusHype
Fast Reinforcement Learning for Anti-jamming Communications—0
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization—0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Regret Bounds for Discounted MDPs—0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
A Tensor Network Approach to Finite Markov Decision Processes—0
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes—0
Machine Learning Approaches For Motor Learning: A Short Review—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Provable Self-Play Algorithms for Competitive Reinforcement Learning—0
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning—0
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Discrete Action On-Policy Learning with Action-Value CriticCode0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons—0
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning—0
Multi-task Reinforcement Learning with a Planning Quasi-Metric—0
BRPO: Batch Residual Policy Optimization—0
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning—0
Conservative Exploration in Reinforcement Learning—0
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Benchmark Results

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