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 10951–11000 of 15113 papers

TitleStatusHype
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks—0
Exploring Unknown States with Action BalanceCode0
Explore and Exploit with Heterotic Line Bundle ModelsCode0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
Automatic Curriculum Learning For Deep RL: A Short Survey—0
Advancing Renewable Electricity Consumption With Reinforcement Learning—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces—0
Stable Policy Optimization via Off-Policy Divergence RegularizationCode0
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Deep Adversarial Reinforcement Learning for Object Disentangling—0
On the Robustness of Cooperative Multi-Agent Reinforcement LearningCode1
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Reinforcement Learning for Combinatorial Optimization: A Survey—0
Convergence of Q-value in case of Gaussian rewards—0
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization—0
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning—0
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning—0
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids—0
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning—0
Distributional Robustness and Regularization in Reinforcement Learning—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
A Geometric Perspective on Visual Imitation Learning—0
Dynamic Experience Replay—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation—0
Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Embodied Synaptic Plasticity with Online Reinforcement learningCode1
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?Code1
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization—0
Robust Market Making via Adversarial Reinforcement LearningCode1
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement LearningCode1
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss—0
Real-World Human-Robot Collaborative Reinforcement Learning—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph—0
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic NavigationCode1
Risk-Averse Learning by Temporal Difference Methods—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning—0
Cluster-Based Social Reinforcement Learning—0
Gaussian Process Policy Optimization—0
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Benchmark Results

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