Thanks for the references So you think basically recording human actions (possibly in some feature extracted way) in part of the 'replay memory' for DQN would

Thanks for the references So you think basically recording human actions (possibly in some feature extracted way) in part of the 'replay memory' for DQN would work well? I have also been reading about deepmind's recent survey of combining deep learning methods with actor critic models What I also want to explore is the possibility to use evolution to evolve q-functions, which shouldn't be so hard to do, rather than evolve policies directly (like in this game). The possibility to evolve self learning machines excite me, rather than just evolving machines in a fixed state. I can also explore whether Darwinian evolution (weights are randomized at birth) is better or worst than Lamarckism evolution where weights are passed to offsprings for. I put some further thoughts and references in my post here http://blog.otoro.net/2015/04/08/evolutionary-function-approximation-for-reinforcement-learning/ http://blog.otoro.net/2015/04/08/evolutionary-function-appro... The surviving agents will be born with a better capacity to learn rather than the capacity to do a predefined job. Stay tuned!