# TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1710.11417 · HTML: https://mlsanity.com/paper/1710.11417/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 12 of 12)

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- [AI planning in the imagination: High-level planning on learned abstract search spaces](https://mlsanity.com/paper/2308.08693/) (2023)
- [AlphaZeroES: Direct score maximization outperforms planning loss minimization](https://mlsanity.com/paper/2406.08687/) (2024)
- [FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL](https://mlsanity.com/paper/2609.34488/) (2026)
- [Learning to Play: Reinforcement Learning and Games](https://mlsanity.com/paper/2610.17391/) (2026)

## Abstract (arXiv)

Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al. 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.