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 3191–3200 of 15113 papers

TitleStatusHype
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand CoresCode1
Laxity-Aware Scalable Reinforcement Learning for HVAC Control—0
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning—0
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Structure in Deep Reinforcement Learning: A Survey and Open Problems—0
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes—0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning—0
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments—0
Automatic Truss Design with Reinforcement LearningCode1
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
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Benchmark Results

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