Model-based versus Model-free Reinforcement Learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Interactive Lecture: Reinforcement Learning

Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Spatial Memory in RL Agents

Delves into spatial memory usage in RL agents for maze navigation tasks, showing improved performance with visual landmarks but inconsistent results in path choosing.

Advanced Machine Learning: Discrete Reinforcement Learning

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

Curiosity and Reward: Neuroscience to AI

Delves into intrinsic motivation, embodied AI, information demand, novelty, and curiosity in decision-making.

Deliberative Agents: Planning and Strategies

Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.

Perceptual Modeling: Understanding Spatial Thinking and Vision

Examines perceptual modeling and spatial thinking in visual intelligence, exploring theories, cognitive maps, and the interplay between bottom-up and top-down processing.

Project-Based Learning in Communications and Robotics

Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.