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 3251–3275 of 15113 papers

TitleStatusHype
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs—0
Data-Efficient Reinforcement Learning in Continuous-State POMDPs—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Creativity in Robot Manipulation with Deep Reinforcement Learning—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach—0
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Data Poisoning Attacks in Contextual Bandits—0
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning—0
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning—0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior—0
Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning—0
Data Valuation for Offline Reinforcement Learning—0
A Tensor Network Approach to Finite Markov Decision Processes—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
DCE: Offline Reinforcement Learning With Double Conservative Estimates—0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
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Benchmark Results

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