Deep learning, reinforcement learning, and world models

Composing an Assistive Control Strategy Based on Linear Bellman Combination From Estimated User's Motor Goal

(2021) Jun-ichiro Furukawa et al.   IEEE Robotics and Automation Letters

Reward is enough

(2021) David Silver et al.   ARTIFICIAL INTELLIGENCE

Forward and inverse reinforcement learning sharing network weights and hyperparameters

(2021) Eiji Uchibe et al.   NEURAL NETWORKS

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

(2020) Caroline J. Charpentier et al.   NEURON

Fast reinforcement learning with generalized policy updates

(2020) André Barreto et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Mastering Atari, Go, chess and shogi by planning with a learned model

(2020) Julian Schrittwieser et al.   NATURE

The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning

(2020) Anis Najar et al.   PLOS BIOLOGY

Towards learning-to-learn

(2019) Benjamin James Lansdell et al.   Current Opinion in Behavioral Sciences

Hierarchical motor control in mammals and machines

(2019) Josh Merel et al.   Nature Communications

Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

(2018) Grady Williams et al.   IEEE Transactions on Robotics

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

(2018) David Silver et al.   SCIENCE

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

(2018) Tatsuya Teramae et al.   Frontiers in Neurorobotics

Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation

(2018) Yoshihisa Tsurumine et al.   ROBOTICS AND AUTONOMOUS SYSTEMS

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

(2017) Sergey Levine et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

The hippocampus as a predictive map

(2017) Kimberly L Stachenfeld et al.   NATURE NEUROSCIENCE

Model-Free Deep Inverse Reinforcement Learning by Logistic Regression

(2017) Eiji Uchibe   NEURAL PROCESSING LETTERS

Neural computations underlying inverse reinforcement learning in the human brain

(2017) Sven Collette et al.   eLife

Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning

(2016) Norikazu Sugimoto et al.   IEEE ROBOTICS & AUTOMATION MAGAZINE

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Dynamic Linear Bellman Combination of Optimal Policies for Solving New Tasks

(2015) Takamitsu MATSUBARA et al.   IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUT

Probabilistic machine learning and artificial intelligence

(2015) Zoubin Ghahramani   NATURE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Dynamic Linear Bellman Combination of Optimal Policies for Solving New Tasks

(2015) Takamitsu MATSUBARA et al.   IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration

(2013) Tingting Zhao et al.   NEURAL COMPUTATION

Darwinian embodied evolution of the learning ability for survival

(2011) Stefan Elfwing et al.   ADAPTIVE BEHAVIOR

Nonparametric representation of an approximated Poincaré map for learning biped locomotion

(2009) Jun Morimoto et al.   AUTONOMOUS ROBOTS

Efficient computation of optimal actions

(2009) Emanuel Todorov   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

(2008) Gen Endo et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

Reinforcement learning of motor skills with policy gradients

(2008) Jan Peters et al.   NEURAL NETWORKS