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 1601–1610 of 15113 papers

TitleStatusHype
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision MakingCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
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Benchmark Results

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