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 12301–12350 of 15113 papers

TitleStatusHype
Dueling Posterior Sampling for Preference-Based Reinforcement LearningCode0
A View on Deep Reinforcement Learning in System Optimization—0
Improving Deep Reinforcement Learning in Minecraft with Action Advice—0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control—0
Reinforcement Learning for Personalized Dialogue Management—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Optimal Attacks on Reinforcement Learning Policies—0
PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning—0
Inverse Reinforcement Learning with Multiple Ranked Experts—0
Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learningCode0
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters—0
Wasserstein Robust Reinforcement Learning—0
Model-Free Unsupervised Learning for Optimization Problems with Constraints—0
Reward Learning for Efficient Reinforcement Learning in Extractive Document SummarisationCode0
Multi-Agent Adversarial Inverse Reinforcement LearningCode0
MineRL: A Large-Scale Dataset of Minecraft DemonstrationsCode0
Goal-Driven Sequential Data Abstraction—0
Hindsight Trust Region Policy OptimizationCode0
Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task AbstractionsCode0
Taxable Stock Trading with Deep Reinforcement Learning—0
Towards Model-based Reinforcement Learning for Industry-near EnvironmentsCode0
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman—0
Large scale continuous-time mean-variance portfolio allocation via reinforcement learning—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment—0
Environment Probing Interaction PoliciesCode0
Google Research Football: A Novel Reinforcement Learning EnvironmentCode0
Interactive Lungs Auscultation with Reinforcement Learning Agent—0
Action Guidance with MCTS for Deep Reinforcement Learning—0
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving—0
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts—0
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks—0
Fairness in Reinforcement Learning—0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
Modeling question asking using neural program generationCode0
Metalearned Neural MemoryCode0
Structured Fusion Networks for DialogCode0
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language InferenceCode0
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey—0
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation—0
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications—0
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning—0
Techniques for Automated Machine Learning—0
Characterizing Attacks on Deep Reinforcement LearningCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Accelerating Reinforcement Learning through GPU Atari EmulationCode0
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Benchmark Results

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