Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Hill Climbing on Value Estimates for Search-control in Dyna
Dyna is an architecture for model-based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search-control, the mechanism to gene…
Model-based Reinforcement LearningReinforcement LearningReinforcement Learning (RL)Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer
We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). T…
Lifelong learningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Learning Powerful Policies by Using Consistent Dynamics Model
Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models via supervised learning. But traditional m…
Atari GamesmodelModel-based Reinforcement LearningMuJoCo+2Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
This work considers the sample and computational complexity of obtaining an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this work, we study the eff…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Machine Learning and System Identification for Estimation in Physical Systems
In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learni…
BIG-bench Machine LearningFrictionModel-based Reinforcement LearningReinforcement Learning+1Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and const…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Combating the Compounding-Error Problem with a Multi-step Model
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a sta…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Switching Linear Dynamics for Variational Bayes Filtering
System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning. Despite their complexity, such systems can often be approximated well by a …
Bayesian InferenceModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+2Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing \emph{full-planning} on Markov Decision Processes (MDPs) built by t…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorit…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M re…
AutoMLBayesian OptimizationBIG-bench Machine LearningEvolutionary Algorithms+6Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds
Lifelong and meta learning aim to learn quickly new skills by transferring knowledge from past experiences. Current algorithms learn a meta-policy from a set of hand-crafted training tasks and adapt their policy to new b…
Knowledge Base ConstructionMeta-LearningModel-based Reinforcement Learningreinforcement-learning+1Deep Residual Reinforcement Learning
We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DD…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Modeling the Long Term Future in Model-Based Reinforcement Learning
In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, th…
Imitation LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+3Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement Learning (RL) algorithms which can improve sampling efficiency by modeling and approximating system dynamics. It has been widely adopted in the r…
Autonomous DrivingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning, Extended version
This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithm…
Model-based Reinforcement LearningReinforcement LearningCurious iLQR: Resolving Uncertainty in Model-based RL
Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for learning control. In this work, we propose a…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the same underlying rules. One reason this is …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Planning with Expectation Models
Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the state and action spaces are large. Expec…
Model-based Reinforcement LearningReinforcement LearningRegularizing Trajectory Optimization with Denoising Autoencoders
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propo…
DenoisingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1