Autocurricula in Multi-Agent Reinforcement Learning
Autocurricula, a key concept in multi-agent experiments, describe the iterative process where agents improve their performance, leading to changes in the environment that affect both themselves and other agents. This cycle results in distinct phases of learning, with each phase building upon the previous one. Autocurricula are especially evident in adversarial settings, where competing groups of agents continually adapt their strategies in response to their opponentsâ actions. For example, in the Hide and Seek game, seekers and hiders continuously evolve their tactics to outsmart each other. This phenomenon mirrors the layered progression observed in cultural and evolutionary processes, where advancements rely on insights gained from earlier stages. Autocurricula offer insights into the dynamic interplay between individual learning and collective intelligence in multi-agent systems.
Multi-Agent Reinforcement Learning in AI
Reinforcement learning (RL) can solve complex problems through trial and error, learning from the environment to make optimal decisions. While single-agent reinforcement learning has made remarkable strides, many real-world problems involve multiple agents interacting within the same environment. This is where multi-agent reinforcement learning (MARL) comes into play, offering a framework for agents to learn, collaborate, and compete, thereby enhancing their collective performance.
This article delves into the concepts, challenges, and applications of Multi-Agent Reinforcement Learning (MARL) in AI.
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