Deep reinforcement learning for multi-agents

2 次查看（过去 30 天）
beni hadi
beni hadi 2020-11-20
评论： beni hadi 2020-11-25
By the multi-agent deep reinforcement learning toolbox, three agents are trained. The reward changes are as shown in the picture. Why do agents' rewards decrease and converge to an unfavorable situation after the reward increases and they move towards desired performance? I expected the process of increasing the rewards and achieving the desired goal to continue as the episode progresses. According to the picture, from episode 700, agents converge to undesired situations, and they didn't change their states.
Thank you.

请先登录，再回答此问题。

采纳的回答

Emmanouil Tzorakoleftherakis
编辑：Emmanouil Tzorakoleftherakis 2020-11-22
Hello,
The policies you will get from RL training change depending on the amount of time the agents spend exploring. Usually, if you see a situation like this where agents converge to a non-ideal solution, you may want to change the agent options to increase exploration.
Hope that helps

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Reinforcement Learning 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by