SARSA Reinforcement Learning

versión 1.0.0.0 (117 KB) por Bhartendu
Maze solving using SARSA, Reinforcement Learning

1,4K descargas

Actualizada 24 May 2017

Ver licencia

Refer to 6.4 (Sarsa: On-Policy TD Control), Reinforcement learning: An introduction, RS Sutton, AG Barto , MIT press
In this demo, two different mazes have been solved by Reinforcement Learning technique, SARSA.
State-Action-Reward-State-Action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning.
SARSA, Updation of Action-Value Function:

Q(S{t}, A{t}) := Q(S{t}, A{t}) + α*[ R{t+1} + γ ∗ Q(S{t+1}, A{t+1}) − Q(S{t}, A{t}) ]

Learning rate (α)
The learning rate determines to what extent the newly acquired information will override the old information. A factor of 0 will make the agent not learn anything, while a factor of 1 would make the agent consider only the most recent information.

Discount factor (γ)
The discount factor determines the importance of future rewards. A factor of 0 will make the agent "opportunistic" by only considering current rewards, while a factor approaching 1 will make it strive for a long-term high reward. If the discount factor meets or exceeds 1, the Q values may diverge.

Note: Convergence is tested on particular examples, in general convergence is not sure for above demo.

Citar como

Bhartendu (2022). SARSA Reinforcement Learning (https://www.mathworks.com/matlabcentral/fileexchange/63089-sarsa-reinforcement-learning), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2016a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!