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 4221–4230 of 15113 papers

TitleStatusHype
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
Adaptive Stress Testing for Autonomous Vehicles—0
Adaptive Stress Testing without Domain Heuristics using Go-Explore—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Adaptive Temporal Difference Learning with Linear Function Approximation—0
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning—0
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Benchmark Results

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