Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model. Alternatively, it can take a more conservative stance and eschew its model in favor of optimizing its behavior solely via real-world interaction. This latter approach can be exceedingly slow to learn from experience, while the former can lead to "planner overfitting" - aspects of the agent's behavior are optimized to exploit errors in its model. This paper explores an intermediate position in which the planner seeks to avoid overfitting through a kind of regularization of the plans it considers. We present three different approaches that demonstrably mitigate planner overfitting in reinforcement-learning environments.
Code (0)
등록된 구현이 없습니다.
Tasks
modelModel-based Reinforcement LearningPositionreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
CORB-Planner: Corridor as Observations for RL Planning in High-Speed Flight
Reinforcement learning (RL) has shown promise in a large number of robotic control tasks. Nevertheless, its deployment on unmanned aerial vehicles (UAVs) remains challenging, mainly because of reliance on accurate dynami…
Reinforcement LearningTrajectory PlanningOvercoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
One of the key challenges that Reinforcement Learning (RL) faces is its limited capability to adapt to a change of data distribution caused by uncertainties. This challenge arises especially in RL systems using deep neur…
GPRReinforcement Learning (RL)CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning
Large Language Model (LLM) agents trained with reinforcement learning (RL) show great promise for solving complex, multi-step tasks. However, their performance is often crippled by "Context Explosion", where the accumula…
Reinforcement LearningAdaptive Data Exploitation in Deep Reinforcement Learning
We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Specifically, ADEPT adaptively manages the us…
Computational EfficiencyDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks
Quantum convolutional neural network (QCNN), an early application for quantum computers in the NISQ era, has been consistently proven successful as a machine learning (ML) algorithm for several tasks with significant acc…