# Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent

## TL;DR

This paper introduces a brain-inspired deep reinforcement learning architecture for a simulated self-driving agent, emphasizing trial-and-error learning, novel network innovations, and improved training efficiency in a controlled environment.

## Contribution

It presents a new deep-Q-network with recurrence and three key innovations, trained using a brain-inspired approach for autonomous driving in simulation.

## Key findings

- The agent achieved advanced driving behaviors after training.
- The proposed methods improved learning speed and handling of sparse rewards.
- The architecture relies solely on raw screen outputs for decision-making.

## Abstract

An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. In the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-partied OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions.