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 11151–11200 of 15113 papers

TitleStatusHype
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces—0
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting—0
Multi Type Mean Field Reinforcement LearningCode1
Temporal-adaptive Hierarchical Reinforcement Learning—0
Soft Hindsight Experience ReplayCode1
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
Social diversity and social preferences in mixed-motive reinforcement learning—0
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning—0
Deep Radial-Basis Value Functions for Continuous Control—0
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision MakingCode1
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning FrameworkCode1
Learning Task-Driven Control Policies via Information Bottlenecks—0
Bootstrapping a DQN Replay Memory with Synthetic Experiences—0
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise—0
Policy Gradient based Quantum Approximate Optimization Algorithm—0
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Effective Diversity in Population Based Reinforcement LearningCode1
Evolutionary algorithms for constructing an ensemble of decision trees—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated DrivingCode1
PolicyGNN: Aggregation Optimization for Graph Neural Networks—0
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement LearningCode0
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine LearningCode1
Preventing Imitation Learning with Adversarial Policy Ensembles—0
Locally Private Distributed Reinforcement Learning—0
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation—0
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning—0
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles—0
Goal-directed graph construction using reinforcement learningCode1
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Distal Explanations for Model-free Explainable Reinforcement Learning—0
Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach—0
Real-time calibration of coherent-state receivers: learning by trial and errorCode0
Some Insights into Lifelong Reinforcement Learning SystemsCode0
Rotation, Translation, and Cropping for Zero-Shot GeneralizationCode0
Unsupervised Program Synthesis for Images By Sampling Without Replacement—0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement LearningCode0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey—0
Tractable Reinforcement Learning of Signal Temporal Logic ObjectivesCode0
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning—0
Constrained Upper Confidence Reinforcement Learning—0
Multitask radiological modality invariant landmark localization using deep reinforcement learningCode0
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment—0
Following Instructions by Imagining and Reaching Visual Goals—0
Pricing commodity swing options—0
EgoMap: Projective mapping and structured egocentric memory for Deep RL—0
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Benchmark Results

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