Explore maximum entropy reinforcement learning in this 42-minute lecture from Pascal Poupart's CS885 course at the University of Waterloo. Delve into key concepts such as encouraging stochasticity, optimal policy, Q-function, and greedy policy. Learn about soft Q-value iteration, soft Q-learning, and soft policy iteration, including policy improvement and proof derivations. Examine the Soft Actor-Critic (SAC) algorithm and its empirical results, with a focus on robustness to environment changes. Access accompanying slides on the course website for a comprehensive understanding of this advanced reinforcement learning topic.
Overview
Syllabus
Intro
Maximum Entropy RL
Reinforcement Learning
Encouraging Stochasticity
Optimal Policy
Q-function
Greedy Policy
Greedy Value function
Soft Q-Value Iteration
Soft Q-learning
Soft Policy Iteration
Policy improvement
Inequality derivation
Proof derivation
Soft Actor-Critic
Soft Actor Critic (SAC)
Empirical Results
Robustness to Environment Changes
Taught by
Pascal Poupart
Related Courses
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Deep Reinforcement Learning in Python
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Modern Reinforcement Learning: Actor-Critic Agents
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Reinforcement Learning with Gymnasium in Python
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Advanced Reinforcement Learning in Python: from DQN to SAC
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Understanding Q* (Q-star) - From Q-Learning to Maximum Entropy Reinforcement Learning
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Sample-based Learning Methods
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