SeqComm-DFL generates value-aware sequential messages via Stackelberg conditioning and trains them end-to-end with decision-focused learning and QMIX to deliver four-to-six times higher rewards on healthcare and SMAC benchmarks.
predict, then optimize
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abstract
We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization. Specifically, we show that softmax consistent action values correspond to optimal entropy regularized policy probabilities along any action sequence, regardless of provenance. From this observation, we develop a new RL algorithm, Path Consistency Learning (PCL), that minimizes a notion of soft consistency error along multi-step action sequences extracted from both on- and off-policy traces. We examine the behavior of PCL in different scenarios and show that PCL can be interpreted as generalizing both actor-critic and Q-learning algorithms. We subsequently deepen the relationship by showing how a single model can be used to represent both a policy and the corresponding softmax state values, eliminating the need for a separate critic. The experimental evaluation demonstrates that PCL significantly outperforms strong actor-critic and Q-learning baselines across several benchmarks.
representative citing papers
A literature survey that organizes diffusion model alignment methods along five axes (feedback source, reward form, optimization mechanism, distribution shift handling, and explicit safety constraints) and identifies open challenges for reliable deployment.
citing papers explorer
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Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication
SeqComm-DFL generates value-aware sequential messages via Stackelberg conditioning and trains them end-to-end with decision-focused learning and QMIX to deliver four-to-six times higher rewards on healthcare and SMAC benchmarks.
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Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey
A literature survey that organizes diffusion model alignment methods along five axes (feedback source, reward form, optimization mechanism, distribution shift handling, and explicit safety constraints) and identifies open challenges for reliable deployment.