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 3201–3210 of 15113 papers

TitleStatusHype
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Agent Probing Interaction Policies—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Data-assimilated model-informed reinforcement learning—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
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Benchmark Results

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