Lecture
Proximal Policy Optimization for Continuous Control
Description
This lecture covers Proximal Policy Optimization (PPO) for continuous control in deep reinforcement learning. It explains the challenges of applying standard policy gradient methods and introduces the idea of PPO to address stability and sample efficiency issues. The lecture delves into the concept of maximizing a surrogate objective function, comparing TRPO and PPO-CLIP approaches. It also discusses Advantage Actor-Critic (A2C) algorithms for improving training stability and efficiency. The instructor emphasizes the importance of updating policy gradients with a fixed learning rate to ensure positive progress. The lecture concludes with a summary highlighting the benefits of using surrogate objectives in policy gradient methods.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (32)
Deep Learning Agents: Reinforcement Learning
Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.
Reinforcement Learning: Basics and Applications
Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.
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.
Monte Carlo Tree Search and Alpha Zero
Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.
Reinforcement Learning: BackUp Diagrams
Introduces the BackUp diagram as a key graphic representation in reinforcement learning.