The provided code appears to be a hybrid reinforcement learning model that combines aspects of model-based (MB) and model-free (MF) learning strategies, a paradigm often used to explain animal and human decision-making. Here's how these concepts relate to biological processes:

Biological Basis

1. Model-Based and Model-Free Learning:

• Model-Free (MF) Learning: This type of learning is akin to habits and relies heavily on historical data of actions-reward associations, which are typically slow to adapt but efficient in terms of computational resources. Biologically, this is often linked to the dorsolateral striatum in the brain, wherein habits and procedural learning are mediated through dopamine signaling.
• Model-Based (MB) Learning: This involves the use of an internal model of the environment to predict future states and outcomes, allowing for flexible and adaptive decision-making. This process is thought to involve the prefrontal cortex and the caudate nucleus, where interactions between neural networks facilitate planning and evaluation of potential future scenarios.

2. Exploration and Exploitation:

• The code includes an explorationFactor, reflective of the exploration-exploitation trade-off inherent in adaptive behavior. Biological systems must balance the use of known strategies (exploitation) with the discovery of new strategies or resources (exploration). Neuromodulators, such as dopamine, play a critical role in regulating this trade-off, with phasic dopamine signaling modulating the balance between the two.

3. Simulation and Iterative Updating:

• The code's use of path simulation and iterative updating of the Q-table can be likened to the biological processes involving trial and error learning where neural circuits are constantly updated based on the received reward feedback. This iterative learning is supported by synaptic plasticity mechanisms (e.g., long-term potentiation) that underlie learning and memory, particularly within the cortico-striatal pathways.

4. Reward Prediction and State Transition:

• In the code, the doActionInModel function likely simulates state transitions and rewards, resembling the way reward prediction errors are processed in the brain. The ventral striatum and midbrain dopaminergic neurons play a central role in processing these prediction errors, updating the value of actions based on discrepancies between expected and received rewards.

5. Persistence of State Information:

• The persistent stateActionVisitCounts in the code mirrors how experiences are accumulated over time, akin to the accumulation of evidence or influence on decision-making in recurrent neural networks. The biological counterpart involves persistent neural activation (e.g., through reverberating circuits) that maintains state information essential for coherent behavior over time.

Conclusion

This model reflects the biological parallel of computational processes involved in decision-making, highlighting the balance between model-based planning and model-free habitual actions. It draws on neurobiological insights into how learning is regulated, how actions are selected based on previous experiences, and how exploration and exploitation strategies are balanced in the brain. The integration of these elements within a simulation framework offers a comprehensive tool for understanding the underlying neural mechanisms of adaptive behavior.