Temporal difference learning

Related lectures (29)

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Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.

Explores actor-critic networks and the advantage actor critic method for optimizing parameters to maximize return.

Explores the SARSA algorithm for reinforcement learning, focusing on updating Q-values and the importance of exploration in learning by rewards.

Explores modeling continuous input spaces in reinforcement learning using neural networks and radial basis functions.

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

Explores safe learning, control, multi-agent coordination, and Nash equilibrium convergence in intelligent systems.

Covers the theory of reinforcement learning, exploring exploration/exploitation dilemma and continuous state/action spaces.

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Introduces the fundamental elements of Reinforcement Learning and demonstrates their application with the Acrobot system.

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