[Paper Review] The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach

Iulian Vlad Serban, Chinnadhurai Sankar+3 more|arXiv (Cornell University)|Jul 12, 2018

Reinforcement Learning in Robotics4 citations

TL;DR

The Bottleneck Simulator is a model-based deep reinforcement learning method that uses a learned, discrete abstract state (bottleneck) to factorize the environment transition model, improving sample efficiency. It outperforms competing methods on few-shot text adventure and real-world dialogue response selection tasks by leveraging structural priors and efficient rollout simulations.

ABSTRACT

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.

Motivation & Objective

• Address the lack of data-efficiency in deep reinforcement learning for real-world applications.
• Improve sample efficiency by learning a factorized, abstract transition model with discrete bottleneck states.
• Enable effective policy learning from few examples by combining model-based rollouts with high-dimensional state representations.
• Provide a mathematical analysis of policy error sources in abstracted MDPs.
• Demonstrate effectiveness on complex, real-world NLP tasks like dialogue response selection and text adventure games.

Proposed method

• The Bottleneck Simulator learns a factorized transition model where states are mapped to discrete abstract (bottleneck) states to reduce model complexity.
• The method uses a discrete, abstract state space as an information bottleneck, mediating transitions between full-world states.
• A policy is trained using Q-learning with rollout simulations based on the learned abstract transition model.
• The abstract state representation is optimized jointly with the transition model for policy performance, not just predictive accuracy.
• The approach allows the policy network to operate on full, high-dimensional states while benefiting from abstraction in the model.
• A fixed-point analysis provides theoretical bounds on policy error, decomposing it into four error sources: structural discrepancy, estimation variance, estimation bias, and model class bias.

Experimental results

Research questions

• RQ1 How can model-based RL be made more data-efficient for real-world, low-data NLP tasks?
• RQ2 To what extent does using discrete abstract states as bottlenecks improve sample efficiency in policy learning?
• RQ3 How do different sources of error—structural, variance, bias, and class bias—affect the performance of the learned policy?
• RQ4 Can the Bottleneck Simulator outperform model-free and other model-based RL methods on complex, real-world dialogue and text-based tasks?
• RQ5 How does incorporating domain-specific structural knowledge into the abstract state space impact performance?

Key findings

• The Bottleneck Simulator achieved state-of-the-art performance on a text adventure game, outperforming competing model-free and model-based methods with significantly fewer training examples.
• On a real-world, complex dialogue response selection task, the Bottleneck Simulator outperformed strong baselines, including model-free DQN and off-policy methods, even with limited human preference data.
• The method demonstrated strong generalization and sample efficiency, achieving high performance with only a few hundred demonstrations.
• The fixed-point analysis revealed that structural discrepancy and model class bias were the dominant error sources, highlighting the importance of meaningful abstraction design.
• Qualitative analysis showed that the learned abstract state structure aligned well with task-relevant semantic concepts, such as game objectives and dialogue goals.
• The approach enabled effective policy learning even when the abstract state space was not perfectly aligned with the true environment structure, due to the policy’s ability to compensate via full-state access.