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 4601–4610 of 15113 papers

TitleStatusHype
Analysis of Reinforcement Learning for determining task replication in workflows—0
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Analysis of Stochastic Processes through Replay Buffers—0
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
Analytically Tractable Bayesian Deep Q-Learning—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
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Benchmark Results

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