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 4476–4500 of 15113 papers

TitleStatusHype
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk—0
Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version)Code0
Safe Reinforcement Learning in Tensor Reproducing Kernel Hilbert Space—0
Optimal Attack and Defense for Reinforcement LearningCode0
Predictable Reinforcement Learning Dynamics through Entropy Rate MinimizationCode0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement LearningCode0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Two-Step Reinforcement Learning for Multistage Strategy Card Game—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Two-step dynamic obstacle avoidanceCode0
An Investigation of Time Reversal Symmetry in Reinforcement LearningCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Replay across Experiments: A Natural Extension of Off-Policy RL—0
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement LearningCode0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and ConstraintsCode0
Digital Twin-Native AI-Driven Service Architecture for Industrial Networks—0
Evaluating Pretrained models for Deployable Lifelong Learning—0
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Benchmark Results

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