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 11851–11900 of 15113 papers

TitleStatusHype
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes—0
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement LearningCode0
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding—0
Deep Reinforcement Learning for Synthesizing Functions in Higher-Order LogicCode0
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile ManipulatorsCode0
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller—0
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement LearningCode0
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition—0
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting BehaviorCode0
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics—0
Robust Visual Domain Randomization for Reinforcement LearningCode0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Attention-based Curiosity-driven Exploration in Deep Reinforcement LearningCode0
Contextual Imagined Goals for Self-Supervised Robotic LearningCode0
Optimizing Percentile Criterion Using Robust MDPs—0
Efficient Decoupled Neural Architecture Search by Structure and Operation SamplingCode0
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning—0
State2vec: Off-Policy Successor Features Approximators—0
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial CriticsCode0
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation—0
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach—0
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination—0
Momentum in Reinforcement Learning—0
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor ControlCode0
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer—0
Policy Optimization for H_2 Linear Control with H_ Robustness Guarantee: Implicit Regularization and Global Convergence—0
Regularization Matters in Policy OptimizationCode0
Application of Reinforcement Learning for 5G Scheduling Parameter Optimization—0
Adversarial Skill Networks: Unsupervised Robot Skill Learning from VideoCode0
IPO: Interior-point Policy Optimization under Constraints—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Dealing with Sparse Rewards in Reinforcement LearningCode0
Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning—0
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning—0
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making -- A Review—0
Policy Learning for Malaria ControlCode0
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement LearningCode0
Towards More Sample Efficiency in Reinforcement Learning with Data AugmentationCode0
Opinion shaping in social networks using reinforcement learning—0
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence ModelCode0
Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids—0
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning—0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics—0
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research—0
On Connections between Constrained Optimization and Reinforcement Learning—0
Multi-View Reinforcement LearningCode0
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation—0
Unsupervised Context Rewriting for Open Domain Conversation—0
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Benchmark Results

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