Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning
Model-based Reinforcement Learning(RL) has shown great advantage in sample-efficiency, but suffers from poor asymptotic performance and high inference cost. A promising direction is to combine model-based reinforcement l…
Model-based Reinforcement LearningMuJoCoreinforcement-learningReinforcement Learning+1Counterfactual Regularization for Model-Based Reinforcement Learning
In sequential tasks, planning-based agents have a number of advantages over model-free agents, including sample efficiency and interpretability. Recurrent action-conditional latent dynamics models trained from pixel-leve…
counterfactualmodelModel-based Reinforcement Learningreinforcement-learning+2Learning by shaking: Computing policy gradients by physical forward-propagation
Model-free and model-based reinforcement learning are two ends of a spectrum. Learning a good policy without a dynamic model can be prohibitively expensive. Learning the dynamic model of a system can reduce the cost of l…
Model-based Reinforcement LearningReinforcement Learning (RL)Policy Optimization In the Face of Uncertainty
Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches. However, existing model-based methods are vulnerable to model bias, which leads to poor generalization and asym…
continuous-controlContinuous ControlModel-based Reinforcement LearningRevisit Policy Optimization in Matrix Form
In tabular case, when the reward and environment dynamics are known, policy evaluation can be written as $\bm{V}_{\bm{\pi}} = (I - \gamma P_{\bm{\pi}})^{-1} \bm{r}_{\bm{\pi}}$, where $P_{\bm{\pi}}$ is the state transitio…
FormModel-based Reinforcement LearningReinforcement LearningPolicy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space
This paper proposes a novel deep reinforcement learning architecture that was inspired by previous tree structured architectures which were only useable in discrete action spaces. Policy Prediction Network offers a way t…
continuous-controlContinuous ControlDeep Reinforcement Learningmodel+5Gradient-Aware Model-based Policy Search
Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can…
modelModel-based Reinforcement LearningReinforcement LearningModel-based Lookahead Reinforcement Learning
Model-based Reinforcement Learning (MBRL) allows data-efficient learning which is required in real world applications such as robotics. However, despite the impressive data-efficiency, MBRL does not achieve the final per…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+4Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics
Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging …
Model-based Reinforcement LearningTowards Model-based Reinforcement Learning for Industry-near Environments
Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optim…
Deep Reinforcement LearningModel-based Reinforcement LearningMuJoCoQ-Learning+3Delegative Reinforcement Learning: learning to avoid traps with a little help
Most known regret bounds for reinforcement learning are either episodic or assume an environment without traps. We derive a regret bound without making either assumption, by allowing the algorithm to occasionally delegat…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
What is a good exploration strategy for an agent that interacts with an environment in the absence of external rewards? Ideally, we would like to get a policy driving towards a uniform state-action visitation (highly exp…
Model-based Reinforcement LearningReinforcement LearningInterpretable Dynamics Models for Data-Efficient Reinforcement Learning
In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Variational Inference MPC for Bayesian Model-based Reinforcement Learning
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge model…
Bayesian InferenceModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+4Benchmarking Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fai…
BenchmarkingmodelModel-based Reinforcement LearningOpen-Ended Question Answering+3Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors…
Model-based Reinforcement LearningReinforcement LearningUncertainty-aware Model-based Policy Optimization
Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches. However, existing model-based methods are vulnerable to model bias, which leads to poor generalization and asym…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+2Exploring Model-based Planning with Policy Networks
Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite the…
BenchmarkingmodelModel-based Reinforcement LearningModel Predictive Control+2When to Trust Your Model: Model-Based Policy Optimization
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Calibrated Model-Based Deep Reinforcement Learning
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inacc…
Deep Reinforcement LearningmodelModel-based Reinforcement LearningMuJoCo+3