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 3171–3180 of 15113 papers

TitleStatusHype
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
Curriculum Learning with a Progression Function—0
Curriculum Offline Imitating Learning—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Agent Probing Interaction Policies—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback—0
A Survey on Reinforcement Learning for Recommender Systems—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