Faster Physics in Python

We're open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of robotics research. This library is one of our core tools for deep learning robotics research, which we've now released as a major version of mujoco-py, our Python 3 bindings for MuJoCo. Many methods in trajectory optimization and reinforcement learning (like LQR, PI2, and TRPO) benefit from being able to run multiple simulations in parallel. Naive usage of the new version's MjSimPool interface shows a 400% speedup over the old, and still about 180% over an optimized and restricted usage pattern using Python's multiprocessing package to gain the same level of parallelism. The majority of the speedup comes from reduced access times to the various MuJoCo data structures.