2 research outputs found
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
We investigate the exploration of an unknown environment when no reward
function is provided. Building on the incremental exploration setting
introduced by Lim and Auer [1], we define the objective of learning the set of
-optimal goal-conditioned policies attaining all states that are
incrementally reachable within steps (in expectation) from a reference
state . In this paper, we introduce a novel model-based approach that
interleaves discovering new states from and improving the accuracy of a
model estimate that is used to compute goal-conditioned policies to reach newly
discovered states. The resulting algorithm, DisCo, achieves a sample complexity
scaling as ,
where is the number of actions, is the number of states
that are incrementally reachable from in steps, and
is the branching factor of the dynamics over such states.
This improves over the algorithm proposed in [1] in both and at
the cost of an extra factor, which is small in most
environments of interest. Furthermore, DisCo is the first algorithm that can
return an -optimal policy for any cost-sensitive
shortest-path problem defined on the -reachable states with minimum cost
. Finally, we report preliminary empirical results confirming our
theoretical findings.Comment: NeurIPS 202