  The information bottleneck principle is an elegant and useful approach to
representation learning. In this paper, we investigate the problem of
representation learning in the context of reinforcement learning using the
information bottleneck framework, aiming at improving the sample efficiency of
the learning algorithms. %by accelerating the process of discarding irrelevant
information when the %input states are extremely high-dimensional. We
analytically derive the optimal conditional distribution of the representation,
and provide a variational lower bound. Then, we maximize this lower bound with
the Stein variational (SV) gradient method. We incorporate this framework in
the advantageous actor critic algorithm (A2C) and the proximal policy
optimization algorithm (PPO). Our experimental results show that our framework
can improve the sample efficiency of vanilla A2C and PPO significantly.
Finally, we study the information bottleneck (IB) perspective in deep RL with
the algorithm called mutual information neural estimation(MINE) . We
experimentally verify that the information extraction-compression process also
exists in deep RL and our framework is capable of accelerating this process. We
also analyze the relationship between MINE and our method, through this
relationship, we theoretically derive an algorithm to optimize our IB framework
without constructing the lower bound.
