May 11, 2024
3 minute read
SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference learning algorithm used in reinforcement learning to estimate the value of a state-action pair. It is an extension of the Q-learning algorithm and was developed by Rummery and Niranjan in 1994. SARSA is an iterative algorithm that learns the value of a state-action pair by repeatedly taking an action in a state and observing the resulting reward and next state. The value of the state-action pair is then updated based on the observed reward and the estimated value of the next state-action pair.
How SARSA Works
SARSA works by maintaining a value function that estimates the value of each state-action pair. The value function is updated after each action is taken, based on the observed reward and the estimated value of the next state-action pair. The update rule for the value function is given by the following equation:
Q(s, a) <= Q(s, a) + α * (r + γ * Q(s', a') - Q(s, a))
where:
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Find a path to becoming a SARSA. Learn more at:
OpenCourser.com/topic/rimsd3/sars
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
SARSA.
Provides a comprehensive overview of reinforcement learning with function approximation, including a chapter on SARSA. It is written by two of the leading researchers in the field and valuable resource for anyone interested in this topic.
Provides a comprehensive overview of multi-agent reinforcement learning, including a chapter on SARSA. It is written by two of the leading researchers in the field and valuable resource for anyone interested in this topic.
Provides a comprehensive overview of real-time dynamic programming, including a chapter on SARSA. It is written by a leading researcher in the field and valuable resource for anyone interested in this topic.
Provides a comprehensive overview of reinforcement learning for robotics, including a chapter on SARSA. It is written by a leading researcher in the field and valuable resource for anyone interested in this topic.
Provides a comprehensive overview of reinforcement learning in games, including a chapter on SARSA. It is written by two of the leading researchers in the field and valuable resource for anyone interested in this topic.
Provides a broad overview of machine learning, including a chapter on reinforcement learning. It is written by a leading researcher in the field and popular textbook for graduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/rimsd3/sars
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