Lifted Bellman Linear Programming for Offline Reinforcement Learning
Quick summary
arXiv:2609.24489v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with exponential moving average (EMA) updates. Multi-step targets incorporate behavior-policy actions and therefore require off-policy correction. We instead impose in-sample Bellman optimality on the critic through inequality constraints. We formulate the Lifted Bellman Linear Program (LBLP), which lifts the linear programming characterization of Bellman optimality to the joint $(Q,V)$ s
Key takeaways
- arXiv:2609.24489v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with exponential moving average (EMA) updates.
- Multi-step targets incorporate behavior-policy actions and therefore require off-policy correction.
- We instead impose in-sample Bellman optimality on the critic through inequality constraints.
Why it matters
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