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 1051–1075 of 15113 papers

TitleStatusHype
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement LearningCode1
Goal-Conditioned Generators of Deep PoliciesCode1
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement LearningCode1
Stabilizing Off-Policy Deep Reinforcement Learning from PixelsCode1
Modular Lifelong Reinforcement Learning via Neural CompositionCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
On the Learning and Learnability of QuasimetricsCode1
Short-Term Plasticity Neurons Learning to Learn and ForgetCode1
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement LearningCode1
Multi-Agent Car Parking using Reinforcement LearningCode1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic CurriculumCode1
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy SimulationsCode1
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive LearningCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic ExplorationCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
SMPL: Simulated Industrial Manufacturing and Process Control Learning EnvironmentsCode1
Fast Population-Based Reinforcement Learning on a Single MachineCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
Training Discrete Deep Generative Models via Gapped Straight-Through EstimatorCode1
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
Transformers are Meta-Reinforcement LearnersCode1
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Benchmark Results

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