At a very high level, the way reinforcement learning works is that the AI attempts to maximise a reward function. This reward function can be summed up as “The sum of all rewards you expect to get in the future”. So using a bunch of maths, the AI looks at the rewards it’s got in the past, the rewards it expects to get in the future, and selects the action that maximises the expected future rewards. The reward function can be defined within the algorithm itself, or come from the environment. For instance, if you want to train a four-legged robot to learn to walk, the reward might be the distance travelled in a certain direction. If you want to train it to play an Atari game, the reward is usually the score.
None of this requires any sort of qualia, or for the agent to want things. It’s a mathematical equation. AI behaves in the way it behaves as a result of the algorithm attempting to maximise it, and the AI can be said to “want” to maximise its reward function or “have the goal of” maximising its reward function because it reliably takes actions to move towards this outcome if it’s a good enough AI.
Functionally. You can regard them all as form of behaviour.
do they depend on qualia, or are they just accompanied by qualia?
This might be a crux, because I’m inclined to think they depend on qualia.
Why does AI ‘behave’ in that way? How do engineers make it ‘want’ to do things?
At a very high level, the way reinforcement learning works is that the AI attempts to maximise a reward function. This reward function can be summed up as “The sum of all rewards you expect to get in the future”. So using a bunch of maths, the AI looks at the rewards it’s got in the past, the rewards it expects to get in the future, and selects the action that maximises the expected future rewards. The reward function can be defined within the algorithm itself, or come from the environment. For instance, if you want to train a four-legged robot to learn to walk, the reward might be the distance travelled in a certain direction. If you want to train it to play an Atari game, the reward is usually the score.
None of this requires any sort of qualia, or for the agent to want things. It’s a mathematical equation. AI behaves in the way it behaves as a result of the algorithm attempting to maximise it, and the AI can be said to “want” to maximise its reward function or “have the goal of” maximising its reward function because it reliably takes actions to move towards this outcome if it’s a good enough AI.