Improving Generative Flow Networks with Path Regularization

• URL: http://arxiv.org/abs/2209.15092v1
• Date: Thu, 29 Sep 2022 20:54:41 GMT
• Title: Improving Generative Flow Networks with Path Regularization
• Authors: Anh Do and Duy Dinh and Tan Nguyen and Khuong Nguyen and Stanley Osher and Nhat Ho
• Abstract summary: Generative Flow Networks (GFlowNets) are recently proposed models for learning policies that generate compositional objects by sequences of actions with the probability proportional to a given reward function. In this work, we propose a novel path regularization method based on optimal transport theory that places prior constraints on the underlying structure of the GFlowNets.
• Score: 8.848799220256366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability proportional to a given reward function. The central problem of GFlowNets is to improve their exploration and generalization. In this work, we propose a novel path regularization method based on optimal transport theory that places prior constraints on the underlying structure of the GFlowNets. The prior is designed to help the GFlowNets better discover the latent structure of the target distribution or enhance its ability to explore the environment in the context of active learning. The path regularization controls the flow in GFlowNets to generate more diverse and novel candidates via maximizing the optimal transport distances between two forward policies or to improve the generalization via minimizing the optimal transport distances. In addition, we derive an efficient implementation of the regularization by finding its closed form solutions in specific cases and a meaningful upper bound that can be used as an approximation to minimize the regularization term. We empirically demonstrate the advantage of our path regularization on a wide range of tasks, including synthetic hypergrid environment modeling, discrete probabilistic modeling, and biological sequence design.

Related papers

• Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization [4.158255103170876]
  GFlowNets are a family of generative models that learn to sample objects proportional to a given reward function. Recent results show a close relationship between GFlowNet training and entropy-regularized reinforcement learning problems. We introduce a simple backward policy optimization algorithm that involves direct sequentially of the value function in an entropy-regularized Markov Decision Process.
  arXiv  Detail & Related papers  (2024-10-20T19:12:14Z)
• On Generalization for Generative Flow Networks [54.20924253330039]
  Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an unnormalized probability distribution. This paper attempts to formalize generalization in the context of GFlowNets, to link generalization with stability, and also to design experiments that assess the capacity of these models to uncover unseen parts of the reward function.
  arXiv  Detail & Related papers  (2024-07-03T13:42:21Z)
• Thompson sampling for improved exploration in GFlowNets [75.89693358516944]
  Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
  arXiv  Detail & Related papers  (2023-06-30T14:19:44Z)
• Stochastic Generative Flow Networks [89.34644133901647]
  Generative Flow Networks (or GFlowNets) learn to sample complex structures through the lens of "inference as control" Existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with dynamics. This paper introduces GFlowNets, a new algorithm that extends GFlowNets to environments.
  arXiv  Detail & Related papers  (2023-02-19T03:19:40Z)
• Distributional GFlowNets with Quantile Flows [73.73721901056662]
  Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a policy for generating complex structure through a series of decision-making steps. In this work, we adopt a distributional paradigm for GFlowNets, turning each flow function into a distribution, thus providing more informative learning signals during training. Our proposed textitquantile matching GFlowNet learning algorithm is able to learn a risk-sensitive policy, an essential component for handling scenarios with risk uncertainty.
  arXiv  Detail & Related papers  (2023-02-11T22:06:17Z)
• Learning GFlowNets from partial episodes for improved convergence and stability [56.99229746004125]
  Generative flow networks (GFlowNets) are algorithms for training a sequential sampler of discrete objects under an unnormalized target density. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. Inspired by the TD($lambda$) algorithm in reinforcement learning, we introduce subtrajectory balance or SubTB($lambda$), a GFlowNet training objective that can learn from partial action subsequences of varying lengths.
  arXiv  Detail & Related papers  (2022-09-26T15:44:24Z)
• Trajectory balance: Improved credit assignment in GFlowNets [63.687669765579585]
  We find previously proposed learning objectives for GFlowNets, flow matching and detailed balance, to be prone to inefficient credit propagation across long action sequences. We propose a new learning objective for GFlowNets, trajectory balance, as a more efficient alternative to previously used objectives. In experiments on four distinct domains, we empirically demonstrate the benefits of the trajectory balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequences and large action spaces.
  arXiv  Detail & Related papers  (2022-01-31T14:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.