  Recent studies have shown that episodic reinforcement learning (RL) is no
harder than bandits when the total reward is bounded by $1$, and proved regret
bounds that have a polylogarithmic dependence on the planning horizon $H$.
However, it remains an open question that if such results can be carried over
to adversarial RL, where the reward is adversarially chosen at each episode. In
this paper, we answer this question affirmatively by proposing the first
horizon-free policy search algorithm. To tackle the challenges caused by
exploration and adversarially chosen reward, our algorithm employs (1) a
variance-uncertainty-aware weighted least square estimator for the transition
kernel; and (2) an occupancy measure-based technique for the online search of a
\emph{stochastic} policy. We show that our algorithm achieves an
$\tilde{O}\big((d+\log (|\mathcal{S}|^2 |\mathcal{A}|))\sqrt{K}\big)$ regret
with full-information feedback, where $d$ is the dimension of a known feature
mapping linearly parametrizing the unknown transition kernel of the MDP, $K$ is
the number of episodes, $|\mathcal{S}|$ and $|\mathcal{A}|$ are the
cardinalities of the state and action spaces. We also provide hardness results
and regret lower bounds to justify the near optimality of our algorithm and the
unavoidability of $\log|\mathcal{S}|$ and $\log|\mathcal{A}|$ in the regret
bound.
