Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Guillermo Infante David Kuric Anders Jonsson Vicenç Gómez Herke van Hoof
Conventional reinforcement learning (RL) methods can successfully solve a
wide range of sequential decision problems. However, learning policies that can
generalize predictably across multiple tasks in a setting with non-Markovian
reward specifications is a challenging problem. We propose to use successor
features to learn a policy basis so that each (sub)policy in it solves a
well-defined subproblem. In a task described by a finite state automaton (FSA)
that involves the same set of subproblems, the combination of these
(sub)policies can then be used to generate an optimal solution without
additional learning. In contrast to other methods that combine (sub)policies
via planning, our method asymptotically attains global optimality, even in
stochastic environments.