Actor-Critic Architecture and Advantage-Actor-Critic

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Related lectures (29)

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Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Reinforcement Learning: Q-Learning

Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.

Continuous Reinforcement Learning: Advanced Machine Learning

Explores continuous-state reinforcement learning challenges, value function estimation, policy gradients, and Policy learning by Weighted Exploration.

Principled Reinforcement Learning with Human Feedback

Delves into Reinforcement Learning with Human Feedback, discussing convergence of estimators and introducing a pessimistic approach for improved performance.

Policy Gradient Methods in Reinforcement Learning

Covers policy gradient methods in reinforcement learning, focusing on optimization techniques and practical applications like the cartpole problem.

TD Learning: Temporal Difference Learning

Covers Temporal Difference Learning, V-values, state-values, and TD methods in reinforcement learning.

Reinforcement Learning: TD Learning and SARSA Variants

Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.

Deep Learning Agents: Reinforcement Learning

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

Linear Programming Techniques in Reinforcement Learning

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.