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 3391–3400 of 15113 papers

TitleStatusHype
Client Selection for Federated Policy Optimization with Environment HeterogeneityCode0
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL—0
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional CurriculumCode1
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning PoliciesCode0
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning—0
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCSCode0
Revisiting the Minimalist Approach to Offline Reinforcement LearningCode1
Cooperation Is All You Need—0
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions—0
Coagent Networks: Generalized and Scaled—0
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Benchmark Results

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