End-to-end Deep Reinforcement Learning for Targeted Drug Generation

2020 ◽  
Author(s):  
Tiago Oliveira Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Reinforcement Learning ◽  
Targeted Drug ◽  
End To End

scholarly journals Verifiably safe exploration for end-to-end reinforcement learning

2021 ◽  
Author(s):  
Nathan Hunt ◽  
Nathan Fulton ◽  
Sara Magliacane ◽  
Trong Nghia Hoang ◽  
Subhro Das ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
End To End

scholarly journals Towards High-Level Intrinsic Exploration in Reinforcement Learning

2020 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
State Of The Art ◽  
Experimental Results ◽  
Prior Work ◽  
Extrinsic Rewards ◽  
Intrinsic Reward ◽  
Long Time ◽  
End To End ◽  
High Level

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

Auto-pipeline

2021 ◽  
Vol 14 (11) ◽  
pp. 2563-2575
Author(s):  
Junwen Yang ◽  
Yeye He ◽  
Surajit Chaudhuri
Keyword(s):  
Reinforcement Learning ◽  
Recent Work ◽  
Pipeline System ◽  
Complex Data ◽  
Data Preparation ◽  
Significant Progress ◽  
Database Table ◽  
End To End

Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to automate multiple such steps end-to-end, by synthesizing complex data-pipelines with both string-transformations and table-manipulation operators. We propose a novel by-target paradigm that allows users to easily specify the desired pipeline, which is a significant departure from the traditional by-example paradigm. Using by-target, users would provide input tables (e.g., csv or json files), and point us to a "target table" (e.g., an existing database table or BI dashboard) to demonstrate how the output from the desired pipeline would schematically "look like". While the problem is seemingly under-specified, our unique insight is that implicit table constraints such as FDs and keys can be exploited to significantly constrain the space and make the problem tractable. We develop an AUTO-PIPELINE system that learns to synthesize pipelines using deep reinforcement-learning (DRL) and search. Experiments using a benchmark of 700 real pipelines crawled from GitHub and commercial vendors suggest that AUTO-PIPELINE can successfully synthesize around 70% of complex pipelines with up to 10 steps.

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