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 12001–12050 of 15113 papers

TitleStatusHype
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—0
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning—0
Transfer of Temporal Logic Formulas in Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Reinforcement Learning and Video Games—0
Sampling Strategies for GAN Synthetic Data—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access LocationsCode0
Discovery of Useful Questions as Auxiliary Tasks—0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal TeachersCode0
Recommendation System-based Upper Confidence Bound for Online Advertising—0
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning—0
Neural Architecture Search in Embedding Space—0
Off-Policy Evaluation in Partially Observable Environments—0
Solving Continual Combinatorial Selection via Deep Reinforcement Learning—0
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains—0
A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World RobotsCode0
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement LearningCode0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems—0
Imitation Learning for Human Pose Prediction—0
Self-driving scale car trained by Deep reinforcement learning—0
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity—0
Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning—0
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement LearningCode0
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning—0
Deep Reinforcement Learning for Control of Probabilistic Boolean NetworksCode0
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation—0
Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs—0
DRLViz: Understanding Decisions and Memory in Deep Reinforcement LearningCode0
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information—0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based ControlCode0
Reinforcement Learning for Joint Optimization of Multiple Rewards—0
Classification with Costly Features as a Sequential Decision-Making ProblemCode0
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning AgentsCode0
Rewarding Coreference Resolvers for Being Consistent with World KnowledgeCode0
Learning Action-Transferable Policy with Action EmbeddingCode0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
No Press Diplomacy: Modeling Multi-Agent GameplayCode0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games—0
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters—0
Answers Unite! Unsupervised Metrics for Reinforced Summarization ModelsCode0
Learning sparse representations in reinforcement learning—0
Learning Dynamic Context Augmentation for Global Entity LinkingCode0
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Benchmark Results

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