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Found 5 papers, 1 papers with code
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
We show that such an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets.
Risk-sensitive Actor-free Policy via Convex Optimization
Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences.
Aiding reinforcement learning for set point control
While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems.
Robust nonlinear set-point control with reinforcement learning
This paper argues that three ideas can improve reinforcement learning methods even for highly nonlinear set-point control problems: 1) Make use of a prior feedback controller to aid amplitude exploration.
Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning
As discussed in the paper, this leads to a separation of the observer dynamics to the recurrent neural network part, and the state feedback to the feedback and feedforward network.
