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
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning—0
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning—0
Goal Exploration Augmentation via Pre-trained Skills for Sparse-Reward Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion—0
Language Control Diffusion: Efficiently Scaling through Space, Time, and TasksCode1
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems—0
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering—0
Meta-Reinforcement Learning Using Model Parameters—0
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion—0
ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy RepresentationCode1
Knowledge-Guided Exploration in Deep Reinforcement Learning—0
Environment Design for Inverse Reinforcement LearningCode0
Low-Rank Modular Reinforcement Learning via Muscle SynergyCode1
Quantum deep recurrent reinforcement learning—0
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications—0
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to RealityCode4
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement LearningCode1
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning—0
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real DataCode1
Teal: Learning-Accelerated Optimization of WAN Traffic EngineeringCode1
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture SearchCode0
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Benchmark Results

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