  The outcome of Jacobian singular values regularization was studied for
supervised learning problems. It also was shown that Jacobian conditioning
regularization can help to avoid the ``mode-collapse'' problem in Generative
Adversarial Networks. In this paper, we try to answer the following question:
Can information about policy conditioning help to shape a more stable and
general policy of reinforcement learning agents? To answer this question, we
conduct a study of Jacobian conditioning behavior during policy optimization.
To the best of our knowledge, this is the first work that research condition
number in reinforcement learning agents. We propose a conditioning
regularization algorithm and test its performance on the range of continuous
control tasks. Finally, we compare algorithms on the CoinRun environment with
separated train end test levels to analyze how conditioning regularization
contributes to agents' generalization.
