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 2561–2570 of 15113 papers

TitleStatusHype
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
Convergence Guarantees for Deep Epsilon Greedy Policy Learning—0
A Meta-Reinforcement Learning Approach to Process Control—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Adaptive patch foraging in deep reinforcement learning agents—0
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Autonomous Warehouse Robot using Deep Q-Learning—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
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Benchmark Results

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