8
  // execute action in environment and get the reward
  agent.learn(reward); // the agent improves its Q,policy,model, etc. reward is a float
}, 0);
</code></pre>

As for the available parameters in the DQN agent `spec`:

- `spec.gamma` is the discount rate. When it is zero, the agent will be maximally greedy and won't plan ahead at all. It will grab all the reward it can get right away. For example, children that fail the marshmallow experiment have a very low gamma. This parameter goes up to 1, but cann