  In this paper, we present our approach to solve a physics-based reinforcement
learning challenge "Learning to Run" with objective to train
physiologically-based human model to navigate a complex obstacle course as
quickly as possible. The environment is computationally expensive, has a
high-dimensional continuous action space and is stochastic. We benchmark state
of the art policy-gradient methods and test several improvements, such as layer
normalization, parameter noise, action and state reflecting, to stabilize
training and improve its sample-efficiency. We found that the Deep
Deterministic Policy Gradient method is the most efficient method for this
environment and the improvements we have introduced help to stabilize training.
Learned models are able to generalize to new physical scenarios, e.g. different
obstacle courses.
