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 5626–5650 of 15113 papers

TitleStatusHype
Logarithmic regret bounds for continuous-time average-reward Markov decision processes—0
Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration EnvironmentsCode0
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement LearningCode1
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning—0
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation—0
Learning to branch with Tree MDPsCode1
Cooperative Reinforcement Learning on Traffic Signal Control—0
Contextual Information-Directed Sampling—0
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner—0
Power and accountability in reinforcement learning applications to environmental policy—0
Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationCode1
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning—0
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward—0
User-Interactive Offline Reinforcement Learning—0
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning—0
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
ARLO: A Framework for Automated Reinforcement LearningCode1
Prototyping three key properties of specific curiosity in computational reinforcement learning—0
Synthesis from Satisficing and Temporal GoalsCode0
Towards biologically plausible Dreaming and Planning in recurrent spiking networksCode0
Long Run Incremental Cost (LRIC) Distribution Network Pricing in UK, advising China's Distribution Network—0
Survey on Fair Reinforcement Learning: Theory and Practice—0
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Benchmark Results

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