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 1261–1270 of 15113 papers

TitleStatusHype
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement LearningCode1
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary StrategiesCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
DittoGym: Learning to Control Soft Shape-Shifting RobotsCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
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Benchmark Results

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