[SB] Sutton & Barto, Reinforcement Learning: An Introduction
[GBC] Goodfellow, Bengio & Courville, Deep Learning
Smith & Gasser, The Development of Embodied Cognition: Six Lessons from Babies
Silver, Huang et al., Mastering the Game of Go with Deep Neural Networks and Tree Search
Houthooft et al., VIME: Variational Information Maximizing Exploration
Stadie et al., Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Bagnell, An Invitation to Imitation
Nguyen, Imitation Learning with Recurrent Neural Networks
Bengio et al., Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
III et al., Searn in Practice
Bojarski et al., End to End Learning for Self-Driving Cars
Guo et al., Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
Rouhollah et al., Learning real manipulation tasks from virtual demonstrations using LSTM
Ross et al., Learning Monocular Reactive UAV Control in Cluttered Natural Environments
Ross et al., A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Ziebart et al., Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior
Abbeel et al., Apprenticeship Learning via Inverse Reinforcement Learning
Ho et al., Model-Free Imitation Learning with Policy Optimization
Finn et al., Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Ziebart et al., Maximum Entropy Inverse Reinforcement Learning
Ziebart et al., Human Behavior Modeling with Maximum Entropy Inverse Optimal Control
Finn et al., Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
Tassa et al., Synthesis and Stabilization of Complex Behaviors through Online Trajectory Optimization
Watter et al., Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Levine et al., Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics
Levine et al., Guided Policy Search
Levine et al., End-to-End Training of Deep Visuomotor Policies
Kumar et al., Learning Dexterous Manipulation Policies from Experience and Imitation
Mishra et al., Prediction and Control with Temporal Segment Models
Lillicrap et al., Continuous control with deep reinforcement learning
Heess et al., Learning Continuous Control Policies by Stochastic Value Gradients
Mordatch et al., Combining model-based policy search with online model learning for control of physical humanoids
Rajeswaran et al., EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Zoph et al., Neural Architecture Search with Reinforcement Learning
Tzeng et al., Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Ganin et al., Domain-Adversarial Training of Neural Networks
Rusu et al., Sim-to-Real Robot Learning from Pixels with Progressive Nets
Hanna et al., Grounded Action Transformation for Robot Learning in Simulation
Christiano et al., Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Xiong et al., Supervised Descent Method and its Applications to Face Alignment
Duan et al., One-Shot Imitation Learning
Lake et al., Building Machines That Learn and Think Like People
Andrychowicz et al., Learning to learn by gradient descent by gradient descent
Finn et al., Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
