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Model BasedReinforcement LearninginAi
+has+proven+successful+in+protein+design%252C+reducing+the+need+for+expensive+wet+lab+experiment+rounds.+However%252C+challenges+persist+in+efficiently+exploring+the+protein+fitness+landscapes+to+identify+optimal+protein+designs.+In+response%252C+we+propose+the+use+of+AlphaZero+to+generate+protein+backbones%252C+meeting+shape+and+structural+scoring+requirements.+We+extend+an+existing+Monte+Carlo+tree+search+(MCTS)+framework+by+incorporating+a+novel+threshold-based+reward+and+secondary+objectives+to+improve+design+precision.+This+innovation+considerably+outperforms+existing+approaches%252C+leading+to+protein+backbones+that+better+respect+structural+scores.+The+application+of+AlphaZero+is+novel+in+the+context+of+protein+backbone+design+and+demonstrates+promising+performance.+AlphaZero+consistently+surpasses+baseline+MCTS+by+more+than+100%2525+in+top-down+protein+design+tasks.+Additionally%252C+our+application+of+AlphaZero+with+secondary+objectives+uncovers+further+promising+outcomes%252C+indicating+the+potential+of+model-based+reinforcement+learning+(RL)+in+navigating+the+intricate+and+nuanced+aspects+of+protein+design&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.8ae176f55ac1320cbd5560279f776fc8%3frik%3d64XX4LY9f3h2oQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Model+Based+Reinforcement+Learning+in+Ai&FORM=IRPRST&ck=67259735E76BC33D928DFF2D04FB8DE2&selectedIndex=80&itb=0)
+has+shown+many+desirable+properties+for+intelligent+control+tasks.+However%252C+satisfying+safety+and+stability+constraints+during+training+and+rollout+remains+an+open+question.+We+propose+a+new+Model-based+RL+framework+to+enable+efficient+policy+learning+with+unknown+dynamics+based+on+learning+model+predictive+control+(LMPC)+framework+with+mathematically+provable+guarantees+of+stability.+We+introduce+and+explore+a+novel+method+for+adding+safety+constraints+for+model-based+RL+during+training+and+policy+learning.+The+new+stability-augmented+framework+consists+of+a+neural-network-based+learner+that+learns+to+construct+a+Lyapunov+function%252C+and+a+model-based+RL+agent+to+consistently+complete+the+tasks+while+satisfying+user-specified+constraints+given+only+sub-optimal+demonstrations+and+sparse-cost+feedback.+We+demonstrate+the+capability+of+the+proposed+framework+through+simulated+experiments.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.80daf9f013042a5b56c4f399397c55ed%3frik%3dXvUpavSKjgJr2g%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Model+Based+Reinforcement+Learning+in+Ai&FORM=IRPRST&ck=616C9340124E45F82E3521F1FE24D997&selectedIndex=105&itb=0)
+for+LLMs%2fRLHF-LLM-reward-model.webp&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.61434ffae5894120dbc94fd339f4ca54%3frik%3d0YhiOzZig0m%252bXw%26pid%3dImgRaw%26r%3d0&exph=1200&expw=1920&q=Model+Based+Reinforcement+Learning+in+Ai&FORM=IRPRST&ck=39AE02469C767325B3ABAA47A9AF8478&selectedIndex=113&itb=0)
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