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 3011–3020 of 15113 papers

TitleStatusHype
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
Asymptotics of Language Model Alignment—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
A Guiding Principle for Causal Decision Problems—0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Asymptotic Bias of Stochastic Gradient Search—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
A Guider Network for Multi-Dual Learning—0
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Benchmark Results

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