A corrective double deep Q-network framework uses encoded message-passing to refine delayed and noisy global states for improved multi-agent control policies.
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AmelPredSto, a stochastic self-predictive representation model, outperforms other state representation learning approaches when combined with actor-critic RL for object-goal navigation in UAVs.
A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
The book introduces the origins, mathematical setup, and optimization stages of RLHF including reward modeling, reinforcement learning, rejection sampling, and direct alignment algorithms.
citing papers explorer
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An Encoded Corrective Double Deep Q-Networks for Multi-Agent Control Systems
A corrective double deep Q-network framework uses encoded message-passing to refine delayed and noisy global states for improved multi-agent control policies.
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Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
AmelPredSto, a stochastic self-predictive representation model, outperforms other state representation learning approaches when combined with actor-critic RL for object-goal navigation in UAVs.
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Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
A new robust Q-CBF framework synthesized via adversarial RL enables safety enforcement on the maximal robust safe set for black-box nonlinear systems.
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Reinforcement Learning from Human Feedback
The book introduces the origins, mathematical setup, and optimization stages of RLHF including reward modeling, reinforcement learning, rejection sampling, and direct alignment algorithms.