Model Based RL Papers/Blogs

• [PDF] Action-Conditional Video Prediction using Deep Networks in Atari Games
• [PDF] Recurrent Environment Simulator
• [PDF] The Predictron: End-To-End Learning and Planning
• [PDF] Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
• [PDF] Value Prediction Network
• [PDF] Classical Planning with Simulators: Results on the Atari Video Games
• [PDF] What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
• [PDF] [Webpage] Model-Based Reinforcement Learning for Atari
• [PDF] Generative Adversarial Networks for Model BasedReinforcement Learning with Tree Search
• [PDF] Planning with Pixels in (Almost) Real Time
• [PDF] Efficient Model–Based Deep Reinforcement Learning withVariational State Tabulation
• [PDF] Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
• [PDF] Model-free, Model-based, and General Intelligence
• [Blog] ApproxiPong: Alpha Go Zero on Pong
• [PDF] [Code] Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Other Related/Interesting Papers

• [PDF] [Blog] [Official Code] [Talk] Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018
• [PDF] [Blog] Neural scene representation and rendering
• [PDF] Image-to-Image Translation with Conditional Adversarial Networks
• [PDF] Point GAN
• [PDF] Autoencoding beyond pixels using a learned similarity metric
• [PDF] Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
• [PDF] [Blog 1] [Blog 2] [Code] U-Net Skip Connections, Deconv explained
• [PDF] [Blog 1] Siamese Neural Networks for One-shot Image Recognition
• [PDF] The Importance of Skip Connections in Biomedical Image Segmentation
• [PDF] Dense Transformer Networks
• [PDF]Accurate Image Super-Resolution Using Very Deep Convolutional Networks
• [PDF] [Blog 1]Self-Attention Generative Adversarial Networks
• [PDF] Video Pixel Networks

Blogs/Articles

• Deep Learning: Perturbations and Diversity is All You Need
• 37 Reasons why your Neural Network is not working
• Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names
• From GAN to WGAN All about GAN losses.
• An Overview of ResNet and its Variants
• Understanding and Implementing Architectures of ResNet and ResNeXt for state-of-the-art Image Classification: From Microsoft to Facebook
• Ganbreeder
• Analogues of mental simulation and imagination in deep learning
• MCTS

Code

• OpenAI Atari Baselines