Meta Reinforcement Learning

Meta-reinforcement learning (Meta-RL) aims to create agents capable of rapidly adapting to new tasks with minimal experience, leveraging prior learning to accelerate adaptation. Current research focuses on improving sample efficiency and generalization across diverse tasks, employing model architectures like recurrent neural networks, transformers, and hypernetworks, along with algorithms such as MAML and PPO, often incorporating techniques like importance sampling and constrained optimization. This field is significant for its potential to enable more robust and adaptable AI systems in various applications, from resource optimization in wireless networks to safe and efficient robot control in unpredictable environments.