Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

\(\gamma\)-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction




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Summary   We train predictive models of environment dynamics with infinite probabilistic horizons using a generative adaptation of temporal difference learning. The resulting \(\gamma\)-model is a hybrid between model-free and model-based mechanisms. Like a value function, it contains information about the long-term future; like a standard predictive model, it is independent of reward.




\(\gamma\)-Model rollouts   Replacing single-step models with \(\gamma\)-models leads to generalizations of the procedures that form the foundation of model-based control. Generalized rollouts have a negative binomial distribution over time per model step. The first step has a geometric distribution from the special case of \(\text{NegBinom}(1,p)=\text{Geom}(1-p)\).




Value estimation   Single-step models estimate values using long model-based rollouts, often between tens and hundreds of steps long. In contrast, values are expectations over a single feedforward pass of a \(\gamma\)-model: \( V(\mathbf{s}_t, \mathbf{a}_t) = \mathbb{E}_{\mathbf{s}_e \sim \mu_\theta(\cdot \mid \mathbf{s}_t, \mathbf{a}_t)}[r(\mathbf{s}_e)] \).




\(\gamma\)-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction