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 5826–5850 of 15113 papers

TitleStatusHype
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning—0
Learning and Information in Stochastic Networks and Queues—0
Learning and Querying Fast Generative Models for Reinforcement Learning—0
Learning and Reasoning for Robot Dialog and Navigation Tasks—0
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning—0
Learning an Embedding Space for Transferable Robot Skills—0
Learning a Policy for Opportunistic Active Learning—0
Learning Approximate and Exact Numeral Systems via Reinforcement Learning—0
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning—0
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents—0
Learning a subspace of policies for online adaptation in Reinforcement Learning—0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Out-of-Distribution-Aware Electric Vehicle Charging—0
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference—0
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy—0
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach—0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets—0
Learning-based MPC from Big Data Using Reinforcement Learning—0
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities—0
Learning-based sensing and computing decision for data freshness in edge computing-enabled networks—0
Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
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Benchmark Results

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