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 8726–8750 of 15113 papers

TitleStatusHype
Quantum Multi-Agent Meta Reinforcement Learning—0
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets—0
Quantum policy gradient algorithms—0
Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning—0
Quantum reinforcement learning in continuous action space—0
Quantum Reinforcement Learning via Policy Iteration—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
Quarl: A Learning-Based Quantum Circuit Optimizer—0
Quasimetric Value Functions with Dense Rewards—0
Quasi-Newton Iteration in Deterministic Policy Gradient—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Quasi-Newton Trust Region Policy Optimization—0
Quasi-optimal Reinforcement Learning with Continuous Actions—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Query The Agent: Improving sample efficiency through epistemic uncertainty estimation—0
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation—0
Queue-based Eco-Driving at Roundabouts with Reinforcement Learning—0
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service—0
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning—0
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning—0
Quinoa: a Q-function You Infer Normalized Over Actions—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
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Benchmark Results

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