Summary of Weber-fechner Law in Temporal Difference Learning Derived From Control As Inference, by Keiichiro Takahashi et al.
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference
by Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to reinforcement learning (RL) by introducing nonlinearity into the update rule based on temporal difference (TD) errors. The standard RL update rule assumes linearity between TD errors and updates, but biological studies suggest that nonlinearities can lead to biases in policy formation. To leverage these nonlinearities, the paper develops a theoretical framework using control as inference, which encompasses various RL and optimal control methods. A key finding is the Weber-Fechner law (WFL), which shows that perception (updates) responds to stimulus change (TD error) with attenuation of increase in stimulus intensity (value function). The authors then propose a practical implementation using a reward-punishment framework and modify optimality definitions. Simulations and robot experiments demonstrate the expected utilities: accelerating rewards early and suppressing punishments during learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how our brains learn from rewards and punishments. It shows that traditional learning rules are too simple and don’t account for the way our brains really work. The researchers propose a new approach that includes nonlinearity in the learning process, which can help us make better decisions. They also find a connection between how we perceive rewards and punishments, showing that as we get more reward or punishment, our perception of it changes. The authors then test their ideas using computer simulations and real robots, demonstrating that their new approach can improve learning. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
