Koopman-assisted RL reformulates max-entropy algorithms using controlled Koopman tensors and reports SOTA performance versus neural SAC on Lorenz, fluid flow, and other systems.
Mag- netic control of tokamak plasmas through deep reinforcement learning
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TreeDQN is a sample-efficient off-policy RL method for combinatorial optimization that uses tree MDPs, requires up to 10 times less training data than on-policy methods, and outperforms state-of-the-art on ML4CO tasks.
citing papers explorer
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Koopman-Assisted Reinforcement Learning
Koopman-assisted RL reformulates max-entropy algorithms using controlled Koopman tensors and reports SOTA performance versus neural SAC on Lorenz, fluid flow, and other systems.
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TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
TreeDQN is a sample-efficient off-policy RL method for combinatorial optimization that uses tree MDPs, requires up to 10 times less training data than on-policy methods, and outperforms state-of-the-art on ML4CO tasks.