Dual learning cycles

[thomashopkins32]

1. Online Learning Cycle (every step):

   • Pros: Rapid adaptation to new experiences, potentially faster learning in dynamic environments.
   • Cons: Higher computational overhead, potential for instability.

2. Offline Learning Cycle (every n steps):

   • Pros: More stable learning, better utilization of past experiences, opportunity for more thorough optimization.
   • Cons: Delayed adaptation to new experiences.

This approach makes sense for several reasons:

• Balanced learning: It combines the quick adaptability of online learning with the stability of offline learning.
• Efficient use of data: The online cycle keeps the agent responsive, while the offline cycle ensures thorough learning from accumulated experiences.
• Stability vs. plasticity: This method addresses the trade-off between quick adaptation (plasticity) and stable performance (stability).
• Computational efficiency: The more intensive offline learning can be scheduled to optimize computational resources.
• Handling non-stationarity: The online cycle helps adapt to changing environments, while the offline cycle reinforces stable knowledge.

Implementation considerations:

• Use a smaller learning rate for the online cycle and a larger one for the offline cycle.
• Consider using different optimizers or hyperparameters for each cycle.
• The offline cycle could use techniques like prioritized experience replay more effectively.
• Ensure the offline buffer is large enough to capture a diverse set of experiences.