Learning GFlowNets from partial episodes for improved convergence and stability

3 code implementations • 26 Sep 2022 • Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfully used for various probabilistic modeling tasks.

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning

1 code implementation • 24 Jan 2022 • Andrei Nica, Khimya Khetarpal, Doina Precup

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices.

Decision Making Hierarchical Reinforcement Learning +2