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 2881–2890 of 15113 papers

TitleStatusHype
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning—0
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning—0
Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning—0
AI Planning: A Primer and Survey (Preliminary Report)—0
A centralized reinforcement learning method for multi-agent job scheduling in Grid—0
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
AIGenC: An AI generalisation model via creativity—0
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Benchmark Results

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