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Deep reinforcement learning for multi-agents

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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.

Accepted Answer

Emmanouil Tzorakoleftherakis
Edited: Emmanouil Tzorakoleftherakis on 22 Nov 2020
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

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