Multi-agent reinforcement learning versus multi-objective reinforcement learning

Question

everyone.

What is the difference between Multi-agent reinforcement learning and Multi-objective reinforcement learning?

And can you explain the pros and cons of the two methods?

I think both methods can serve some objectives...

Answer 1

Multiple-agents and multiple-objectives are orthogonal concepts. They can be combined together.

Examples of multiple-objectives:

• Optimizing for both space and time
• e.g. trading bot: Maximize profit while maintaining risk-neutrality
• self-driving car: Control steering, acceleration, and braking while keeping me safe and the ride comfortable. - Smoothness + safety
• human: Chew-gum and walk simultaneously
• robot: Walk with a human like gait, without wearing the joints too much, unless it's really important, then you can push it a little

Agents are independent decision-making systems, like individual players in a game. Multiple agents can cooperate or compete with each other, in either shared or separate environments.

Cooperatively, they seek to maximize at least two objectives, their individual performance while also maximizing the objective of the team's performance. This is a multi-objective problem in a multi-agent problem.

Most production systems have multiple objectives either implicitly or explicitly.