  Standard RL algorithms assume fixed environment dynamics and require a
significant amount of interaction to adapt to new environments. We introduce
Policy-Dynamics Value Functions (PD-VF), a novel approach for rapidly adapting
to dynamics different from those previously seen in training. PD-VF explicitly
estimates the cumulative reward in a space of policies and environments. An
ensemble of conventional RL policies is used to gather experience on training
environments, from which embeddings of both policies and environments can be
learned. Then, a value function conditioned on both embeddings is trained. At
test time, a few actions are sufficient to infer the environment embedding,
enabling a policy to be selected by maximizing the learned value function
(which requires no additional environment interaction). We show that our method
can rapidly adapt to new dynamics on a set of MuJoCo domains. Code available at
https://github.com/rraileanu/policy-dynamics-value-functions.
