# Dynamic Planning Networks

## TL;DR

Dynamic Planning Networks (DPN) integrate model-based and model-free reinforcement learning to enable efficient online planning, significantly reducing the number of state-transitions needed and demonstrating improved performance and generalization.

## Contribution

DPN introduces a novel architecture that dynamically constructs plans using learned models, combining search strategies with deep learning for improved RL efficiency.

## Key findings

- Reduces state-transitions during planning by up to 96%
- Emergent classical search patterns like BFS and DFS
- Improves data efficiency, performance, and generalization

## Abstract

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. In contrast to model-free methods, model-based planning lets the agent efficiently test action hypotheses without performing costly trial-and-error in the environment. DPN learns to efficiently form plans by expanding a single action-conditional state transition at a time instead of exhaustively evaluating each action, reducing the required number of state-transitions during planning by up to 96%. We observe various emergent planning patterns used to solve environments, including classical search methods such as breadth-first and depth-first search. DPN shows improved data efficiency, performance, and generalization to new and unseen domains in comparison to several baselines.