Proximal Policy Optimization for Amortized Discrete Sampling
Researchers have introduced Proximal Policy Optimization (PPO) as a novel method for training Generative Flow Networks (GFlowNets). This approach leverages connections between GFlowNets and entropy-regularized reinforcement learning to derive policy gradient algorithms. The paper demonstrates that PPO offers improved convergence speed and data efficiency compared to existing GFlowNet training objectives across various benchmarks, including molecular graph generation. AI
IMPACT Introduces a more efficient training method for generative models, potentially accelerating research in areas like molecular discovery.
- GFlowNets
- arXiv
- Proximal Policy Optimization
- reinforcement learning
- policy gradient algorithms
- Amortized Discrete Sampling
- molecular graph generation
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