We explore the use of Evolution Strategies (ES), a class of black box
optimization algorithms, as an alternative to popular MDP-based RL techniques
such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show
that ES is a viable solution strategy that scales extremely well with the
number of CPUs available: By using a novel communication strategy based on
common random numbers, our ES implementation only needs to communicate scalars,
making it possible to scale to over a thousand parallel workers. This allows us
to solve 3D humanoid walking in 10 minutes and obtain competitive results on
most Atari games after one hour of training. In addition, we highlight several
advantages of ES as a black box optimization technique: it is invariant to
action frequency and delayed rewards, tolerant of extremely long horizons, and
does not need temporal discounting or value function approximation.
Contains implementation of: Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. “Evolution Strategies as a Scalable Alternative to Reinforcement Learning”. Arxiv.org. https://arxiv.org/pdf/1703.03864.pdf.