  One of the challenges in online reinforcement learning (RL) is that the agent
needs to trade off the exploration of the environment and the exploitation of
the samples to optimize its behavior. Whether we optimize for regret, sample
complexity, state-space coverage or model estimation, we need to strike a
different exploration-exploitation trade-off. In this paper, we propose to
tackle the exploration-exploitation problem following a decoupled approach
composed of: 1) An "objective-specific" algorithm that (adaptively) prescribes
how many samples to collect at which states, as if it has access to a
generative model (i.e., a simulator of the environment); 2) An
"objective-agnostic" sample collection exploration strategy responsible for
generating the prescribed samples as fast as possible. Building on recent
methods for exploration in the stochastic shortest path problem, we first
provide an algorithm that, given as input the number of samples $b(s,a)$ needed
in each state-action pair, requires $\tilde{O}(B D + D^{3/2} S^2 A)$ time steps
to collect the $B=\sum_{s,a} b(s,a)$ desired samples, in any unknown
communicating MDP with $S$ states, $A$ actions and diameter $D$. Then we show
how this general-purpose exploration algorithm can be paired with
"objective-specific" strategies that prescribe the sample requirements to
tackle a variety of settings -- e.g., model estimation, sparse reward
discovery, goal-free cost-free exploration in communicating MDPs -- for which
we obtain improved or novel sample complexity guarantees.
