  Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent
solution on the Internet for multimedia streaming and responsible for the
majority of global traffic. DASH uses adaptive bit rate (ABR) algorithms, which
select the video quality considering performance metrics such as throughput and
playout buffer level. Pensieve is a system that allows to train ABR algorithms
using reinforcement learning within a simulated network environment and is
outperforming existing approaches in terms of achieved performance. In this
paper, we demonstrate that the performance of the trained ABR algorithms
depends on the implementation of the simulated environment used to train the
neural network. We also show that the used congestion control algorithm impacts
the algorithms' performance due to cross-layer effects.
