  Many of the challenges facing today's reinforcement learning (RL) algorithms,
such as robustness, generalization, transfer, and computational efficiency are
closely related to compression. Prior work has convincingly argued why
minimizing information is useful in the supervised learning setting, but
standard RL algorithms lack an explicit mechanism for compression. The RL
setting is unique because (1) its sequential nature allows an agent to use past
information to avoid looking at future observations and (2) the agent can
optimize its behavior to prefer states where decision making requires few bits.
We take advantage of these properties to propose a method (RPC) for learning
simple policies. This method brings together ideas from information
bottlenecks, model-based RL, and bits-back coding into a simple and
theoretically-justified algorithm. Our method jointly optimizes a latent-space
model and policy to be self-consistent, such that the policy avoids states
where the model is inaccurate. We demonstrate that our method achieves much
tighter compression than prior methods, achieving up to 5x higher reward than a
standard information bottleneck. We also demonstrate that our method learns
policies that are more robust and generalize better to new tasks.
