# Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

## TL;DR

This paper presents a unified framework combining planning and deep reinforcement learning for tactical decision making in autonomous driving, demonstrating improved performance over baseline methods in simulated highway scenarios.

## Contribution

It extends the AlphaGo Zero algorithm to continuous state spaces and integrates Monte Carlo tree search with deep reinforcement learning for autonomous driving decisions.

## Key findings

- Outperforms baseline methods in simulated highway scenarios
- Combining planning and learning yields better results than using either alone
- Extended AlphaGo Zero to continuous state spaces for autonomous driving

## Abstract

Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.