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 4281–4290 of 15113 papers

TitleStatusHype
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning—0
Tackling Visual Control via Multi-View Exploration Maximization—0
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage ProbabilityCode1
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Is Conditional Generative Modeling all you need for Decision-Making?—0
Continuous Episodic Control—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
Applying Deep Reinforcement Learning to the HP Model for Protein Structure PredictionCode0
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Benchmark Results

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