Welcome. I have analyzed your submission on 'Multi-Agent Reinforcement Learning in Stochastic Environments'. We will now begin the TKA.
In section 3.2, you describe the 'Reward Shaping' mechanism. How does your agent manage the trade-off between immediate exploration and long-term convergence in highly non-stationary environments?
We implemented a decaying epsilon-greedy strategy combined with a prioritized replay buffer to ensure the agent focuses on high-variance transitions that fall outside the current policy constraints.
Correct. Now, look at your 'Methodology' section, Equation 4. Why did you choose a Laplacian prior instead of a Gaussian one for the noise distribution?
We observed heavy-tailed noise in the sensor data. A Gaussian prior would have underestimated the outlier frequency, leading to overfitting.
ASAI Bot is analyzing response for conceptual DNA...
