… policy distillation for transferring one or more action policies … expert policies can be combined
into a single multi-task policy that … process by continually distilling the best policy to a target …

… entire landscape of policy distillation, … distillation techniques, that are preferred depending
on specifics of the task. Specifically a newly proposed expected entropy regularised distillation …

… peer policy will lead to policy improvement through a view of hypothetical hybrid policy. Then
based on our theoretical results, we present a disadvantageous policy distillation objective …

… Data sampling strategies: We evaluate the effect of two different sampling strategies to create
Dπi for policy distillation. Data sampling is a key component as the sampled dataset should …

… trained teacher policies [6]. Existing PD approaches mostly treat the student policy training
as … prioritize those important frames in the PD training to enhance the distilled student policy? …

… Combined with policy distillation, we show that DVM enables agents to visit different regions
of the state space, combine the information, and continue learning. In order to perform DVM …

… behaves under an expert policy. In the second reinforcement learning phase, we fine-tune
the model through policy learning, while distilling representations from the teacher. This way, …

… Then we introduce our policy distillation framework and the corresponding policy optimization
algorithm in details. Without loss of generality, we take liquidation, ie, to sell a specific …

… Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm … policy
optimization. GPO uses imitation learning for policy crossover in the state space and applies policy …

… the distilled policy πDi . With the aggregation of several distillation datasets, we can distill
several policies into … we call a model where policy 1 and policy 2 have been distilled in, πD1,2 . …