Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless LLM inference in edge computing, this stu…
Computational EfficiencyEdge-computingModel-based Reinforcement LearningTrust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
Dyna-style model-based reinforcement learning (MBRL) combines model-free agents with predictive transition models through model-based rollouts. This combination raises a critical question: 'When to trust your model?'; i.…
modelModel-based Reinforcement LearningMuJoCoAdaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation
Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to …
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+1Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents
In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its adaptiveness to local changes in the environment. In reinforcement learning, howev…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningSafe Deep Model-Based Reinforcement Learning with Lyapunov Functions
Model-based Reinforcement Learning (MBRL) has shown many desirable properties for intelligent control tasks. However, satisfying safety and stability constraints during training and rollout remains an open question. We p…
Model-based Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement LearningNeuromorphic dreaming: A pathway to efficient learning in artificial agents
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspir…
Model-based Reinforcement LearningGenerating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling code instead of LLMs for planning has pot…
Code GenerationLanguage ModellingLarge Language ModelModel-based Reinforcement Learning+3iVideoGPT: Interactive VideoGPTs are Scalable World Models
World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing re…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Efficient Multi-agent Reinforcement Learning by Planning
Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample eff…
Computational EfficiencyModel-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learning+3Learning Latent Dynamic Robust Representations for World Models
Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate agent's knowledge about the underlying dynamics of the environment, enabling learning a world model as a useful planner. However, top MBRL agents s…
Model-based Reinforcement LearningModel-based reinforcement learning for protein backbone design
Designing protein nanomaterials of predefined shape and characteristics has the potential to dramatically impact the medical industry. Machine learning (ML) has proven successful in protein design, reducing the need for …
modelModel-based Reinforcement LearningProtein Designreinforcement-learning+2Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation
We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless network sites. Given a sequence of action spaces represented by overlappin…
Model-based Reinforcement LearningCompilerDream: Learning a Compiler World Model for General Code Optimization
Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code…
DiversityModel-based Reinforcement Learningreinforcement-learningValue prediction+1A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
This note clarifies some confusions (and perhaps throws out more) around model-based reinforcement learning and their theoretical understanding in the context of deep RL. Main topics of discussion are (1) how to reconcil…
Model-based Reinforcement LearningActive Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system. It is based on a longstanding pipeline familiar to the control community in which one performs experiments on the environment…
Active LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningModel-based Reinforcement Learning for Parameterized Action Spaces
We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Decision Processes (PAMDPs). The agent lear…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement LearningActive Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Efficiently tackling multiple tasks within complex environment, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions, such as reinforcement lear…
Active LearningBayesian InferenceModel-based Reinforcement Learningreinforcement-learning+3Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards
Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy. A commonly used simplifying assumption is that the dynamics and reward both exhibit the same …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningDeep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncert…
Gaussian ProcessesModel-based Reinforcement LearningRobust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
We introduce $\mathcal{L}_1$-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function …
Model-based Reinforcement LearningMuJoCoreinforcement-learning