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
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
When Do Transformers Shine in RL? Decoupling Memory from Credit AssignmentCode2
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance—0
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations—0
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement LearningCode0
Generative Job Recommendations with Large Language Model—0
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning—0
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
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Benchmark Results

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