4 papers
Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs
Mianchu Wang, Giovanni Montana
Retrosynthesis planning aims to decompose target molecules into available building blocks, forming a synthetic tree where each internal node represents an intermediate compound and…
Temporal Encoding Strategies for Energy Time Series Prediction
Aayam Bansal, Keertan Balaji, Zeus Lalani
In contemporary power systems, energy consumption prediction plays a crucial role in maintaining grid stability and resource allocation enabling power companies to minimize energy…
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
Mianchu Wang, Rui Yang, Xi Chen +3
Offline Goal-Conditioned RL (GCRL) offers a feasible paradigm for learning general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, t…
Goal-conditioned Offline Reinforcement Learning through State Space Partitioning
Mianchu Wang, Yue Jin, Giovanni Montana
Offline reinforcement learning (RL) aims to infer sequential decision policies using only offline datasets. This is a particularly difficult setup, especially when learning to achi…