Deep Reinforcement Learning: Mini-Batches and Policy Methods

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Covers MuZero, a model that learns to predict rewards and actions iteratively, achieving state-of-the-art performance in board games and Atari video games.

Learning-aided Program Reasoning

Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

Proximal Policy Optimization for Continuous Control

Explores Proximal Policy Optimization for enhancing stability and efficiency in continuous control with deep reinforcement learning.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Collective Learning Dynamics: Similarity Exploitation

Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.

Subtracting the mean reward via the value function

Covers the significance of subtracting the mean reward in policy gradient methods for deep reinforcement learning, reducing noise in the stochastic gradient.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.