In the seminal work on generalization for deep reinforcement learning (link), there were many experiments performed to understand the effect of algorithmic additions on generalization, as follows: To help answer your second question, another work (link) determined that vanilla reinforcement learning algorithms such as A2C and PPO performed better than some other reinforcement learning algorithms specifically constructed to tackle the problem of generalization. I suggest first experimenting with some of the algorithmic additions mentioned above since many are fairly simple to implement. If those do not suffice, then I recommend scanning the literature for novel algorithms that have been developed since those two papers were written (2018) that aim to overcome the problem of generalization. *Results: Our initial 2018 support process review yielded 87 KTCwO. 36 of them achieved minimal weight loss while 22 gained weight, with no change in percent of wait list patients inactive due to obesity. Since the multistep algorithm introduction, 28 listed inactive KTCwO enrolled. 11 of them with less than 6-month follow up were excluded from analysis. 7 17 patients have achieved weight loss with >2 follow up encounters since initial evaluation. 3 patients have implemented diet and exercise regimens and 4 patients have been referred to weight center. Of the weight center referrals, one patient is managed with medication and three have been referred for bariatric surgical evaluations.
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