What algorithm can improve this project more?

[bmaxdk]

DQN (Deep Q-Network): a deep reinforcement learning algorithm that combines Q-Learning with deep neural networks. It can handle high-dimensional state spaces and non-linear function approximations.

A3C (Asynchronous Advantage Actor-Critic): a deep reinforcement learning algorithm that uses parallel training of multiple actors to stabilize the training process and speed up convergence.

PPO (Proximal Policy Optimization): a reinforcement learning algorithm that uses a trust region optimization method to update the policy parameters. It is known for its stability and sample efficiency.

TRPO (Trust Region Policy Optimization): a reinforcement learning algorithm that uses a trust region optimization method to update the policy parameters and ensure stability during training.

These algorithms, along with MADDPG, are some of the most commonly used algorithms in reinforcement learning, and each has its own strengths and weaknesses.

MADDPG is provides great solution for solving multi-agent problem. What are better algorithm can be used?