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

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Showing 3251–3300 of 15113 papers

TitleStatusHype
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust ControlCode2
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method—0
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources—0
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning—0
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning—0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
Multi-market Energy Optimization with Renewables via Reinforcement Learning—0
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement LearningCode0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Kernelized Reinforcement Learning with Order Optimal Regret Bounds—0
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense FeedbackCode0
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning—0
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
Robust Reinforcement Learning through Efficient Adversarial Herding—0
Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization—0
Online Prototype Alignment for Few-shot Policy TransferCode0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds—0
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous DrivingCode1
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles—0
Policy Regularization with Dataset Constraint for Offline Reinforcement LearningCode1
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel—0
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation—0
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement LearningCode1
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning—0
Learning Not to Spoof—0
Approximate information state based convergence analysis of recurrent Q-learning—0
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Decoupled Prioritized Resampling for Offline RLCode1
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning—0
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RLCode1
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline DataCode1
CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments—0
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control TheoryCode1
Mildly Constrained Evaluation Policy for Offline Reinforcement LearningCode0
Model-Based Reinforcement Learning with Multi-Task Offline PretrainingCode0
Boosting Offline Reinforcement Learning with Action Preference Query—0
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation—0
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlCode2
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Benchmark Results

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