Talk:State–action–reward–state–action

Date

When did this algorithm get invented ? XApple 19:46, 7 May 2007 (UTC)

First published 1994, added info. 220.253.135.178 16:50, 21 May 2007 (UTC)

Hey, thanks a lot for contributing to wikipedia ! XApple 23:05, 27 May 2007 (UTC)

Updates

For updates, SARSA uses the next action chosen, not the best next action, to reflect the value of the last state/action under the current policy. If using the best next action, you'll end up with Watkin's Q-Learning which SARSA was an attempt to provide an alternative to. By updating with the value of the best next action (Watkin's Q-Learning) the update can possibly over-estimate values, as the control method used will not pick this action all the time (due to the need to balance exploration and exploitation). A comparison between Q-Learning and SARSA, perhaps Cliff World from Rich Sutton's 'Reinforcement Learning An Introduction' (1998), may be useful to clarify the differences and the resulting behaviour --131.217.6.6 08:17, 29 May 2007 (UTC)

this is the algorithm presented in Q-Learning:

$Q(s_{t},a_{t})\leftarrow Q(s_{t},a_{t})+\alpha _{t}(s_{t},a_{t})[r_{t}+\gamma \max _{a}Q(s_{t+1},a)-Q(s_{t},a_{t})]$

SARSA:

$Q(s_{t},a_{t})\leftarrow Q(s_{t},a_{t})+\alpha [r_{t+1}+\phi Q(s_{t+1},a_{t+1})-Q(s_{t},a_{t})]$

Uses "backpropagation"? updates previous Q entry with future reward? Dspattison (talk) 19:20, 19 March 2008 (UTC)

Correct Algorithm ?

Is the algorithm given correct? Should it not be R(t) not R(t+1) ? I've looked at and that seems to support what Thrun & Norvig teach in their Stanford ai-class wheeliebin (talk) 04:58, 12 November 2011 (UTC)

Note also section 1 where the page states "Taking every letter in the quintuple" it lists "R(t+1)." Shouldn't this be "R(t)" as well? -- RDK