Multi-Agent Reinforcement Learning frameworks emphasize accountability by designing environments where agents learn optimal behaviors through interaction and feedback. These frameworks incorporate mechanisms for traceability of decisions and allow the inclusion of reward structures that incentivize responsible and cooperative behavior, thereby addressing accountability in complex, agentic AI systems.
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