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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 - MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
www.natolambert.com/papers/2021-mbrl-lib

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. Apr 20, 2021. | Luis Pineda, Brandon Amos, Amy Zhang, Nathan O Lambert, Roberto Calandra. Tags: ABSTRACT. : hide & show. Model-based reinforcement learning is a compelling framework for

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 - 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 - 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 - 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 - ML/RL & Microrobotics | Blog
www.natolambert.com/writing/ml-rl-microrobotics

Berkeley), older version. (UC Berkeley). Slide from my quals summarizing this problem. 2. History: Model-based Reinforcement Learning (MBRL) as a case study. This is the level of the stack where I took a total leap of faith in the spring of 2018. We

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

Nato's Update | April 2021: Research Foundations
www.natolambert.com/u/2021-04

and collaboration followed by promotion and new connections would be optimal in the long term. Some done things: Media & Academic. 2021 has so many of my projects being released! At 4 already 🚀, 3 more are close. We released. MBRL-Lib: A Modular Library