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 5876–5900 of 15113 papers

TitleStatusHype
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning—0
Learning Eco-Driving Strategies at Signalized Intersections—0
Learning Efficient Multi-Agent Cooperative Visual Exploration—0
Learning Efficient Navigation in Vortical Flow Fields—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Learning Efficient Recursive Numeral Systems via Reinforcement Learning—0
Learning Efficient Representations for Reinforcement Learning—0
Learning Elimination Ordering for Tree Decomposition Problem—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Learning Equational Theorem Proving—0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Learning Extreme Hummingbird Maneuvers on Flapping Wing Robots—0
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards—0
Learning fast changing slow in spiking neural networks—0
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients—0
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models—0
Learning Force Control for Legged Manipulation—0
Learning for Visual Navigation by Imagining the Success—0
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning—0
Learning from Demonstrations using Signal Temporal Logic—0
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
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Benchmark Results

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