let env = MountainCar::default();
let n_actions = env.action_space().card().into();

let mut rng = StdRng::seed_from_u64(0);
let (mut ql, policy) = {
    let basis = Fourier::from_space(5, env.state_space()).with_bias();
    let q_func = make_shared(LFA::vector(basis, SGD(0.001), n_actions));
    let policy = Greedy::new(q_func.clone());

    (QLearning {
        q_func,
        gamma: 0.9,
    }, policy)
};

for e in 0..200 {
    // Episode loop:
    let mut j = 0;
    let mut env = MountainCar::default();
    let mut action = policy.sample(&mut rng, env.emit().state());

    for i in 0.. {
        // Trajectory loop:
        j = i;

        let t = env.transition(action);

        ql.handle(&t).ok();
        action = policy.sample(&mut rng, t.to.state());

        if t.terminated() {
            break;
        }
    }

    println!("Batch {}: {} steps...", e + 1, j + 1);
}

let traj = MountainCar::default().rollout(|s| policy.mode(s), Some(500));

println!("OOS: {} states...", traj.n_states());