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 5951–6000 of 15113 papers

TitleStatusHype
Understanding the Evolution of Linear Regions in Deep Reinforcement LearningCode0
OSS Mentor A framework for improving developers contributions via deep reinforcement learning—0
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer SamplingCode0
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook—0
Opportunistic Episodic Reinforcement Learning—0
MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System—0
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
Learning General World Models in a Handful of Reward-Free Deployments—0
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation—0
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal—0
Climate Change Policy Exploration using Reinforcement Learning—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Epistemic Monte Carlo Tree Search—0
Towards Quantum-Enabled 6G Slicing—0
On the connection between Bregman divergence and value in regularized Markov decision processes—0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks—0
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents—0
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables—0
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All NetworksCode0
Implicit Offline Reinforcement Learning via Supervised Learning—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes—0
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning—0
Task Phasing: Automated Curriculum Learning from DemonstrationsCode0
Model-based Lifelong Reinforcement Learning with Bayesian ExplorationCode0
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Safe Policy Improvement in Constrained Markov Decision Processes—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes—0
Scaling Laws for Reward Model Overoptimization—0
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining—0
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation—0
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization—0
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control—0
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning—0
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement LearningCode0
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness—0
Learning Preferences for Interactive AutonomyCode0
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification—0
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design—0
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes—0
CLUTR: Curriculum Learning via Unsupervised Task Representation LearningCode0
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with DemonstrationsCode0
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity—0
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Benchmark Results

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