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 11901–11950 of 15113 papers

TitleStatusHype
REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH—0
Reinforcement Learning with Chromatic Networks—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Temporal Difference Weighted Ensemble For Reinforcement Learning—0
Zero-Shot Policy Transfer with Disentangled Attention—0
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning—0
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout—0
Reinforcement learning for suppression of collective activity in oscillatory ensembles—0
Multi-task Batch Reinforcement Learning with Metric Learning—0
ROBEL: Robotics Benchmarks for Learning with Low-Cost RobotsCode0
Model Imitation for Model-Based Reinforcement Learning—0
C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement LearningCode0
Data Valuation using Reinforcement LearningCode0
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real TransferCode1
Efficient Inference and Exploration for Reinforcement Learning—0
Avoidance Learning Using Observational Reinforcement Learning—0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
Active inference: demystified and comparedCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network—0
Controlling an Autonomous Vehicle with Deep Reinforcement Learning—0
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches—0
Paying Attention to Function WordsCode0
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?—0
Modular Deep Reinforcement Learning with Temporal Logic SpecificationsCode0
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement LearningCode0
Constrained Attractor Selection Using Deep Reinforcement Learning—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
PAC Reinforcement Learning without Real-World Feedback—0
Where to Look Next: Unsupervised Active Visual Exploration on 360° Input—0
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze—0
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement LearningCode0
Deep Reinforcement Learning with Modulated Hebbian plus Q Network ArchitectureCode0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?—0
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning—0
Bayesian Optimization for Iterative LearningCode0
Meta-Inverse Reinforcement Learning with Probabilistic Context VariablesCode0
Redirection Controller Using Reinforcement Learning—0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Instance-dependent _-bounds for policy evaluation in tabular reinforcement learning—0
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control—0
MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning—0
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator—0
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Benchmark Results

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