Example doesn't "learn" anything

[sigaloid]

Running the example and adding some debugging code, I'm finding that the neural network is not learning anything at all.

    let mut trainer = AgentTrainer::new();
    let mut agent = MyAgent {
        state: MyState { x: 0, y: 0 },
    };
    trainer.train(
        &mut agent,
        &QLearning::new(0.2, 0.01, 2.),
        &mut FixedIterations::new(10000000),
        &RandomExploration::new(),
    );
    let state1 = MyState { x: 1, y: 0 };
    let state2 = MyState { x: 0, y: 1 };
    let actions = vec![MyAction { dx: 0, dy: -1 }, MyAction { dx: -1, dy: 0 }];
    for action in actions {
        println!(
            "1: {:?} {:?} {:?}",
            state1,
            action,
            trainer.expected_value(&state1, &action),
        );
        println!(
            "2: {:?} {:?} {:?}",
            state2,
            action,
            trainer.expected_value(&state2, &action),
        );
        println!();
    }

1: MyState { x: 1, y: 0 } MyAction { dx: 0, dy: -1 } Some(-13.582118848154376)
2: MyState { x: 0, y: 1 } MyAction { dx: 0, dy: -1 } Some(-14.27795681221249)

1: MyState { x: 1, y: 0 } MyAction { dx: -1, dy: 0 } Some(-14.27795681221249)
2: MyState { x: 0, y: 1 } MyAction { dx: -1, dy: 0 } Some(-13.582118848154376)

It seems that it hasn't learned that even with x:1 and y:0, dx:-1 and dy:0 is the best move. Am I misunderstanding the example or anything here?

[milanboers]

It needs to arrive at 10,10 and you are at 1,0. Both dx:-1,dy:0 and dx:0,dy:-1 are moving away from the target. The best move is either dx:1,dy:0 or dx:0,dy:1.

[sigaloid]

Oh okay, I see. I misunderstood the goal. thank you very much!