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 4161–4170 of 15113 papers

TitleStatusHype
Adaptive Aggregation for Safety-Critical Control—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Adaptive Batch Size for Safe Policy Gradients—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing—0
Adaptive control of a mechatronic system using constrained residual reinforcement learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning—0
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Benchmark Results

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