CS885 Module 2: Maximum Entropy Reinforcement Learning Published 2020-06-08 Download video MP4 360p Recommendations 30:20 CS885 Module 3: Imitation Learning 1:16:10 L1 MDPs, Exact Solution Methods, Max-ent RL (Foundations of Deep RL Series) 1:02:31 Soft Actor Critic is Easy in PyTorch | Complete Deep Reinforcement Learning Tutorial 13:24 The Principle of Maximum Entropy 22:18 CS885 Module 1: Trust region & proximal policy optimization 59:36 Policy Gradient Theorem Explained - Reinforcement Learning 1:07:30 MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) 40:47 Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial 1:03:32 John Schulman - Reinforcement Learning from Human Feedback: Progress and Challenges 1:22:38 CS480/680 Lecture 19: Attention and Transformer Networks 1:27:30 MIT 6.S094: Deep Reinforcement Learning for Motion Planning 1:07:46 Everything You Need to Know About Deep Deterministic Policy Gradients (DDPG) | Tensorflow 2 Tutorial 31:52 Soft Actor Critic (V2) 25:21 L4 TRPO and PPO (Foundations of Deep RL Series) 1:17:00 CS885 Lecture 8b: Bayesian and Contextual Bandits 41:08 Deep RL Bootcamp Lecture 10B Inverse Reinforcement Learning 1:06:12 Can a Random Reinforcement Learning Agent Maximize its Score? Soft Actor Critic (SAC) in Tensorflow2 1:40:41 CS480/680 Lecture 18: Recurrent and recursive neural networks 38:58 CS480/680 Lecture 20: Autoencoders Similar videos 49:31 Paper Club with Peter - Maximum Entropy Inverse Reinforcement Learning 37:46 CS885 Module 6: Inverse RL 19:59 Inverse Reinforcement Learning Explained 23:28 CS885 Module 5: Distributional RL 11:29 Maximum Entropy Inverse Reinforcement Learning Part 1 5:54:32 Reinforcement Learning Course: Intro to Advanced Actor Critic Methods 1:25:48 CSL Spring'21 - Lecture 13: Inverse Reinforcement Learning 14:22 CS 285: Lecture 20, Inverse Reinforcement Learning, Part 4 30:32 CS885 Presentation - Actor-Attention-Critic for Multi-Agent Reinforcement Learning 31:59 CS 285: Lecture 16, Part 1: Offline Reinforcement Learning 2 17:10 CS885 Lecture 19a: End-to-end LSTM based dialog control (Presenter: Hamidreza Shahidi) 1:17:30 Lecture 19 RL as Inference 1 18:14 CS885 Lecture 15b: Proximal Policy Optimization (Presenter: Ruifan Yu) More results