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Found 4 papers, 1 papers with code
Optimally Teaching a Linear Behavior Cloning Agent
The goal of the teacher is to teach a realizable target policy to the learner using minimum number of state demonstrations.
Provable Defense against Backdoor Policies in Reinforcement Learning
Instead, our defense mechanism sanitizes the backdoor policy by projecting observed states to a 'safe subspace', estimated from a small number of interactions with a clean (non-triggered) environment.
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning
Our TDim results provide the minimum number of samples needed for reinforcement learning, and we discuss their connections to standard PAC-style RL sample complexity and teaching-by-demonstration sample complexity results.
On the relationship between multitask neural networks and multitask Gaussian Processes
Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN.
