DAgger
An interactive imitation learning algorithm proposed by Stéphane Ross et al. in 2011. It addresses the compounding error problem of behavioral cloning by having an expert re-label the states actually visited by the agent, progressively shifting the training data distribution toward the agent's own visitation distribution.
Timeline (last 90 days)
The DAgger (Dataset Aggregation) method has experts provide corrective labels for biased states during model execution, in order to address the distribution shift problem of behavior cloning.
DAgger (Dataset Aggregation), proposed by Ross et al. in 2011, has the core idea of collecting the model's true state distribution at test time and continuously appending expert feedback to the training set in order to address the covariate shift problem.
Distilling the search gains back into the policy network via a distributional value target combined with the DAgger method yielded a win rate improvement of +0.045 (1,600 matches, p = 0.007).
DAgger mitigates distribution shift by interactively requesting expert annotations during policy execution, but requires continuous online expert involvement.
DAgger (Dataset Aggregation) is an interactive learning algorithm that mitigates distribution shift by interactively requesting expert annotations during policy execution.
The DAgger algorithm alleviates the distribution shift problem in imitation learning through iterative requests for expert corrections.
Stéphane Ross等人提出了DAgger算法以缓解行为克隆的分布偏移问题
DAgger算法由Stéphane Ross等人提出，通过在训练中持续让学生模型产生新轨迹并请求教师标注来缓解分布偏移
Stéphane Ross等人提出DAgger算法，通过训练中持续让学生模型产生新轨迹并请求教师标注来缓解行为克隆的分布偏移问题
All Facts (9)
The DAgger algorithm alleviates the distribution shift problem in imitation learning through iterative requests for expert corrections.
70%UnverifiedThe DAgger (Dataset Aggregation) method has experts provide corrective labels for biased states during model execution, in order to address the distribution shift problem of behavior cloning.
50%UnverifiedDAgger (Dataset Aggregation), proposed by Ross et al. in 2011, has the core idea of collecting the model's true state distribution at test time and continuously appending expert feedback to the training set in order to address the covariate shift problem.
50%UnverifiedDistilling the search gains back into the policy network via a distributional value target combined with the DAgger method yielded a win rate improvement of +0.045 (1,600 matches, p = 0.007).
50%UnverifiedDAgger mitigates distribution shift by interactively requesting expert annotations during policy execution, but requires continuous online expert involvement.
50%UnverifiedDAgger (Dataset Aggregation) is an interactive learning algorithm that mitigates distribution shift by interactively requesting expert annotations during policy execution.
50%UnverifiedStéphane Ross等人提出了DAgger算法以缓解行为克隆的分布偏移问题
50%UnverifiedDAgger算法由Stéphane Ross等人提出，通过在训练中持续让学生模型产生新轨迹并请求教师标注来缓解分布偏移
50%UnverifiedStéphane Ross等人提出DAgger算法，通过训练中持续让学生模型产生新轨迹并请求教师标注来缓解行为克隆的分布偏移问题
50%