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Lambert - Exploitation Exploration (in MBRL) | Blog
www.natolambert.com/writing/exploitation-exploration

Exploitation Exploration (in MBRL). A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric. January 25, 2021. | Machine Learning. Model-based RL does this wonky thing where it explores by

Lambert - On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
www.natolambert.com/papers/2021-hyperparams-mbrl

Kurtland Chua, Frank Hutter, Roberto Calandra. Tags: ABSTRACT. : hide & show. Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynamics

Lambert - Debugging Deep Model-based Reinforcement Learning Systems | Blog
www.natolambert.com/writing/debugging-mbrl

Debugging Deep Model-based Reinforcement Learning Systems. April 5, 2021. | Machine Learning. I saw an. example. of this debugging lessons for model-free RL and felt fairly obliged to repeat it for model-based RL (MBRL). Ultimately MBRL is so much younger

Lambert - Objective Mismatch in Model-based Reinforcement Learning
www.natolambert.com/papers/2020-objective-mismatch-mbrl

(MBRL) is a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focused on using more advanced function approximators and planning schemes, with little development of the general framework. In this

Lambert - Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
www.natolambert.com/papers/2019-low-level-mbrl

model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e. without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at

Lambert - Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
www.natolambert.com/papers/2020-nonholonomic-yaw-control-mbrl

Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning. IEEE Robotics and Automation Letters. Dec 21, 2020. | Nathan Lambert, Craig Schindler, Daniel S Drew, Kristofer SJ Pister. Tags: ABSTRACT. : hide & show. Non

Lambert - Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
www.natolambert.com/papers/2020-long-term-dynamics

trajectory-based models yield significantly more accurate long term predictions, improved sample efficiency, and ability to predict task reward. What you need to know: Current methods for predicting into the future of MBRL are not thematically matched

Nathan Lambert | Mental Health Advocation
www.natolambert.com/mental-health

(Spring 2018 — Fall 2020). Constrained optimization for control in MBRL, (Tried a couple times — Summer 2019). Audio transmission with electrohydrodynamic thrusters, (Summer 2018 — Spring 2019). Multiple projects on controlled coordination and learning

Nato's Update | March 2021: Building
www.natolambert.com/u/2021-03

control). From May, I will reset some of these processes with an internship at. DeepMind. Some done things: Media & Academic. I was on the. TalkRL podcast. on Model-based RL, Trajectory-based models, Quadrotor control, Hyperparameter Optimization for MBRL

Lambert - Robot learning, model-based RL, and related optimization at NeurIPs 2020 | Blog
www.natolambert.com/writing/neurips-2020

in every numerical/data-driven method because we do not have infinite data. I was making connections during this talk to the problems of model-based learning and how it is very hard to disambiguate uncertainty introduced by the model. In a way, MBRL could