Continuous Reinforcement Learning: Advanced Machine Learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 3 of 3

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Autonomous Vehicles: Trajectory Prediction and Social Behavior

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Manipulating Objects with Robots: Vision-Language Integration

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.

Monte-Carlo Methods for Reinforcement Learning

Explores Monte-Carlo methods for reinforcement learning, comparing them with TD-methods and emphasizing the efficiency of TD methods in propagating information.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Deep and Robust Reinforcement Learning Techniques

Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.

Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained

Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.