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Reinforcement learning algorithms
(Elsevier, 2024) Tareq, Wadhah Zeyad; Amasyalı, Mehmet Fatih
The training scheme is a critical fundamental in multiagent systems, especially with reinforcement learning methods. The reinforcement learning agent builds its experience through interacting with the environment by trial and error. Later, the agent uses these experiences to decide which is the correct action and which one is not. In multiagent systems, two or more agents learn through trial and error in the same environment. These agents can cooperate to perform a single task or to compete to achieve a single goal. The training of multiple agents has many challenges. Selecting a suitable training scheme is one of these challenges. This chapter examines different schemes to find out the optimal scheme for training multiagent deep reinforcement learning. All applied schemes concentrated on two main fundamentals: centralized and distributed. All schemes tested on self-driving filed with multiple autonomous vehicles. Different traffic scenarios are utilized to measure the impact of each scheme in different situations. In the experiments, three different schemes were tested: centralized, distributed, and hybrid. The results show that the combined model (hybrid) achieves better performance compared with standard models.