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 11801–11850 of 15113 papers

TitleStatusHype
Improving Sample Efficiency in Model-Free Reinforcement Learning from ImagesCode1
AI Assisted Annotator using Reinforcement Learning—0
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots—0
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem—0
Generating Paraphrases with Lean Vocabulary—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement LearningCode0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems—0
Machine Translation for Machines: the Sentiment Classification Use Case—0
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement LearningCode1
Generalization in Generation: A closer look at Exposure Bias—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning—0
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning—0
RLCache: Automated Cache Management Using Reinforcement Learning—0
Multiagent Rollout Algorithms and Reinforcement LearningCode0
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
Relational Graph Learning for Crowd NavigationCode0
MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental DynamicsCode0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Deep Reinforcement Learning Based Power control for Wireless Multicast Systems—0
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement LearningCode0
Deep Coordination GraphsCode0
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals—0
Playing Atari Ball Games with Hierarchical Reinforcement Learning—0
Safe Reinforcement Learning on Autonomous Vehicles—0
SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning—0
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling—0
CAQL: Continuous Action Q-Learning—0
MERL: Multi-Head Reinforcement Learning—0
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Scaling data-driven robotics with reward sketching and batch reinforcement learning—0
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
Harnessing Structures for Value-Based Planning and Reinforcement LearningCode0
Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation—0
Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement—0
Solving single-objective tasks by preference multi-objective reinforcement learning—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Self-Supervised State-Control through Intrinsic Mutual Information RewardsCode0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution—0
Partial Simulation for Imitation Learning—0
Long-term planning, short-term adjustments—0
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning—0
Meta Learning via Learned Loss—0
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients—0
Policy Optimization by Local Improvement through Search—0
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Benchmark Results

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