Beginner Explanation
Imagine your brain is like a big library filled with books. Each book represents a different action you can take, and the information inside tells you how good that action is based on your past experiences. Synaptic Q-learning is like a librarian who updates the books every time you learn something new. If you try a new activity and it goes well, the librarian makes a note in the book to say, ‘This is a good action!’ If it doesn’t go well, the note says, ‘Maybe avoid this next time.’ This way, over time, you get better at choosing the best actions based on what you’ve learned.
Technical Explanation
Synaptic Q-learning is a reinforcement learning algorithm that leverages the principles of Q-learning but focuses on updating action-value functions at a synaptic level, inspired by biological neural networks. The core of Q-learning involves the Bellman equation, which updates the Q-value for an action taken in a given state based on the reward received and the maximum expected future rewards. In a synaptic context, the update rule can be expressed as:
Q(s, a) ← Q(s, a) + α[r + γ max Q(s’, a’) – Q(s, a)]
where α is the learning rate, r is the reward received, γ is the discount factor, s is the current state, a is the action taken, and s’ is the next state. In synaptic Q-learning, these updates can be modeled as changes in synaptic weights, reflecting how the strength of connections between neurons changes with learning, thus mimicking biological processes in the brain.
Academic Context
Synaptic Q-learning integrates concepts from reinforcement learning and neurobiology, particularly the Hebbian learning principle, which states that ‘cells that fire together, wire together.’ This approach is grounded in the mathematical framework of the Bellman equations, which are central to dynamic programming and Markov Decision Processes (MDPs). Key papers in this area include ‘Reinforcement Learning: An Introduction’ by Sutton and Barto, which outlines the foundational principles of Q-learning, and research exploring the biological plausibility of reinforcement learning algorithms, such as ‘A Biological Interpretation of Reinforcement Learning’ by Dayan and Niv. The intersection of these fields opens new avenues for understanding learning processes in both artificial and biological systems.
View Source: https://arxiv.org/abs/2511.16066v1