Row 8862

Row ID: 8862 | Dataset Entry | Axioma AXP Content Repository

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I've been thinking a lot about reinforcement learning (RL) and had what I think is a pretty obvious idea.

The primary issue with most reinforcement-learning frameworks is that they assume a stationary reward. An assumption which leads to suboptimal or counterproductive behavior in RL agents as they overfit to specific rewards. As a result, we have AI that is blindly driven towards goals without accounting for changes in the environment.

I believe this problem stems in large part from misconceptions about the dopamine system and how the "reward function" operates in biological entities. Most people seem to assume that dopamine is the primary agent driving human learning and seeking behavior, but experiments show that even when dopamine is suppressed, we will still seek, for example continue to eat, And we retain our ability to learn. This would seem to indicate that our understanding of dopamine as the "root" of the reward function is inaccurate.

It's well established that dopamine release diminishes with repeated exposure to the same stimulus. You can't keep pursuing the same goal and making marginal gains indefinitely without eventually losing interest. Rather than acting like some kind of catecholaminergic id that drives us blindly towards behavior, it serves an adaptive function. By decreasing the novelty and reward value of a stimulus over time, it prevents us from single-mindedly pursuing a reward to the point of self-detriment.

It would seem only natural to factor for this in reinforcement-learning models. To create an adaptive reward function that decreases the value of repetitive actions. Essentially, as an agent repetitively pursues the same goal, the reward it perceives is progressively diminished.

Essentially, by mimicking the natural reduction in interest and reward that humans experience over time, the agent would be prevented from fixating on a single objective. Making it impossible for any "runaway" AI scenario to take place.

Though.. I understand this would have its own problems. Even in this framework dopamine is still oversimplified. A diminishing reward function may make it so that the model loses motivation, fails to learn effectively, and never performs well at all. Exploration is still a critical factor - the AI would need to be motivated to explore new strategies, for example.

But I think those issues could be solved.

tl;dr AI can't convert the entire mass of Earth into paperclips if it gets bored of making paperclips

FieldValue
text I've been thinking a lot about reinforcement learning (RL) and had what I think is a pretty obvious idea. The primary issue with most reinforcement-learning frameworks is that they assume a stationary reward. An assumption which leads to suboptimal or counterproductive behavior in RL agents as they overfit to specific rewards. As a result, we have AI that is blindly driven towards goals without accounting for changes in the environment. I believe this problem stems in large part from misconcep…
label r/openai
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communityName r/OpenAI
datetime 2024-05-20
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Raw Record

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  "text": "I've been thinking a lot about reinforcement learning (RL) and had what I think is a pretty obvious idea.\n\nThe primary issue with most reinforcement-learning frameworks is that they assume a stationary reward. An assumption which leads to suboptimal or counterproductive behavior in RL agents as they overfit to specific rewards. As a result, we have AI that is blindly driven towards goals without accounting for changes in the environment.\n\nI believe this problem stems in large part from misconceptions about the dopamine system and how the \"reward function\" operates in biological entities. Most people seem to assume that dopamine is the primary agent driving human learning and seeking behavior, but experiments show that even when dopamine is suppressed, we will still seek, for example continue to eat, And we retain our ability to learn. This would seem to indicate that our understanding of dopamine as the \"root\" of the reward function is inaccurate. \n\nIt's well established that dopamine release diminishes with repeated exposure to the same stimulus. You can't keep pursuing the same goal and making marginal gains indefinitely without eventually losing interest. Rather than acting like some kind of catecholaminergic id that drives us blindly towards behavior, it serves an adaptive function. By decreasing the novelty and reward value of a stimulus over time, it prevents us from single-mindedly pursuing a reward to the point of self-detriment.\n\nIt would seem only natural to factor for this in reinforcement-learning models. To create an adaptive reward function that decreases the value of repetitive actions. Essentially, as an agent repetitively pursues the same goal, the reward it perceives is progressively diminished. \n\nEssentially, by mimicking the natural reduction in interest and reward that humans experience over time, the agent would be prevented from fixating on a single objective. Making it impossible for any \"runaway\" AI scenario to take place. \n\nThough.. I understand this would have its own problems. Even in this framework dopamine is still oversimplified. A diminishing reward function may make it so that the model loses motivation, fails to learn effectively, and never performs well at all. Exploration is still a critical factor - the AI would need to be motivated to explore new strategies, for example. \n\nBut I think those issues could be solved. \n\ntl;dr AI can't convert the entire mass of Earth into paperclips if it gets bored of making paperclips",
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