Summary of Random Policy Evaluation Uncovers Policies Of Generative Flow Networks, by Haoran He and Emmanuel Bengio and Qingpeng Cai and Ling Pan
Random Policy Evaluation Uncovers Policies of Generative Flow Networks
by Haoran He, Emmanuel Bengio, Qingpeng Cai, Ling Pan
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Generative Flow Network (GFlowNet) is a probabilistic framework that learns to sample objects with probability proportional to an unnormalized reward function. This framework shares similarities with reinforcement learning (RL), which aims to maximize rewards. Recent works have explored connections between GFlowNets and maximum entropy RL, but the relationship between GFlowNets and standard RL remains largely unexplored. The paper bridges this gap by revealing a fundamental connection between GFlowNets and policy evaluation in RL. Surprisingly, the value function obtained from evaluating a uniform policy is closely associated with the flow functions in GFlowNets. A rectified random policy evaluation (RPE) algorithm is introduced, which achieves the same reward-matching effect as GFlowNets based on simply evaluating a fixed random policy. Empirical results demonstrate that RPE achieves competitive results compared to previous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GFlowNet is a new way for machines to learn and make decisions. It’s like a game where the machine tries to find objects with certain rewards. This framework is connected to another area of learning called reinforcement learning, which also tries to maximize rewards. But until now, nobody has looked at how these two areas are related. The paper shows that GFlowNets are actually very close to one part of reinforcement learning called policy evaluation. This discovery leads to a new way of doing things called rectified random policy evaluation (RPE). RPE is able to achieve the same results as GFlowNets, but in a simpler way. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
