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 8101–8150 of 15113 papers

TitleStatusHype
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning—0
MICo: Improved representations via sampling-based state similarity for Markov decision processesCode0
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningCode1
Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework—0
LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning—0
Grounding Complex Navigational Instructions Using Scene Graphs—0
Towards Learning to Play Piano with Dexterous Hands and Touch—0
Offline Reinforcement Learning as One Big Sequence Modeling ProblemCode1
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement LearningCode0
Safe RAN control: A Symbolic Reinforcement Learning Approach—0
Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour—0
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning—0
Towards Deeper Deep Reinforcement Learning with Spectral Normalization—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making—0
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Ad Headline Generation using Self-Critical Masked Language Model—0
Quantitative Day Trading from Natural Language using Reinforcement Learning—0
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning—0
A Coarse to Fine Question Answering System based on Reinforcement Learning—0
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning—0
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs—0
Reward is enough for convex MDPs—0
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs—0
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning—0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning—0
Deep Reinforcement Learning in Quantitative Algorithmic Trading: A ReviewCode0
Q-attention: Enabling Efficient Learning for Vision-based Robotic ManipulationCode1
Shaped Policy Search for Evolutionary Strategies using Waypoints—0
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture—0
Predictive Representation Learning for Language Modeling—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning—0
On the Theory of Reinforcement Learning with Once-per-Episode Feedback—0
Reinforcement Learning for on-line Sequence Transformation—0
Reinforcement Learning reveals fundamental limits on the mixing of active particles—0
Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning—0
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm—0
A nearly Blackwell-optimal policy gradient methodCode0
Learning Approximate and Exact Numeral Systems via Reinforcement Learning—0
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics MixtureCode1
Stochastic Intervention for Causal Inference via Reinforcement Learning—0
Task-Guided Inverse Reinforcement Learning Under Partial Information—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
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Benchmark Results

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