CausalBioRL: Reinforcement Learning with Causal World Models for Autonomous Drug Discovery
Njeri, Kelyn Paul;
CausalBioRL: Reinforcement Learning with Causal World Models for Autonomous Drug Discovery
Model-based RL framework integrating causal discovery, structural causal models, and do-calculus planning for drug discovery. Features a DrugDiscovery-v0 Gymnasium environment (244D obs, 130D action), hierarchical planner (UCB1 + CEM), adaptive reward learner, and surrogate docking. Benchmarked against PPO, SAC, and random baselines across 3 environments.
Hierarchial Planning, Drug discovery, Reinforcement learning, Drug Discovery, Structural Causal Models, causal inference, Molecular Generation
Hierarchial Planning, Drug discovery, Reinforcement learning, Drug Discovery, Structural Causal Models, causal inference, Molecular Generation
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selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
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