Reinforcement Learning is a type of machine learning where an AI agent improves its performance through trial and error by taking actions within a specific setting. The AI gets positive feedback (rewards) when it does something good and negative feedback (penalties) when it makes a mistake, helping it figure out the best approach over time. Through this process, it can learn to master complex tasks like winning games, navigating mazes, or teaching a robot to walk.
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Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
Lifelong Robotic Reinforcement Learning by Retaining Experiences
Lifelong Robotic Reinforcement Learning by Retaining Experiences
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Autonomous Reinforcement Learning via Subgoal Curricula
Autonomous Reinforcement Learning via Subgoal Curricula