Pathologies of Approximate Policy Iteration in Dynamic ... - MIT
Recommend Documents
where G vk is a greedy policy w.r.t. vk, TÏk is the Bell- man operator associated to ..... this operator is a γ-contraction in max-norm (Bert- sekas & Tsitsiklis, 1996 ...
Jul 6, 2008 - ... algorithms have been proposed for learning good or even optimal policies Sutton and Barto (1998). 3 ...... Richard Sutton and Andrew Barto.
Abstract. We consider the classical policy iteration method of dynamic
programming (DP), where ... and chattering, and optimistic and distributed policy
iteration.
... and has hence attracted a great deal of interest within the control community. ... (API) has generated much interest recently within the RL community [7, 3]. ..... Athena Scientific, Nashua, NH. 4. ... Ph.D. thesis, Kings College, Cambridge, UK.
Dec 8, 2014 - Approximate Modified Policy Iteration and its Application to the Game of Tetris. Journal of. Machine Learning Research, Microtome Publishing, ...
Belmont, Massachusetts, 1995. [5] D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont,. Massachusetts, 1996. [6] A. M. ...
iteration [1] and policy iteration [2] are two popular DP methods and have been used ... Step 1: Simulate a policy and update its value function as follows after the ...
In John D. Lafferty, Christopher K. I.. Williams, John Shawe-Taylor ...... problem of online SVM learning has been found by Cauwenberghs and Poggio [70] where.
Jun 22, 2008 - strated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car
Theodore J. Perkins. Department of ... processes, Perkins and Pendrith [10] showed that observation-action values that are fixed ..... MIT Press/Bradford. Books ...
Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA. (e-mail: ...... Belmont, MA: Athena Scientific, 2007, vol. II. [9] F. Blanchini, âSet ...
Jul 26, 2016 - all descriptors for all supevoxels is costly. Thus, a sparse section of supervoxels and choosing their most useful and least costly descriptors is ...
To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, ...
The vector r can be viewed as a cost vector of an aggregate problem that has s states ... or (1.7) is solved approximately, with a finite number of iterations of some ...... to C(λ) and d(λ), respectively, are obtained from the following recursive
random MDPs, the chain walk, the inverted pendulum, and a con- tinuous maze .... formed well in terms of wall clock time: to compute a policy with. 100,000 ...
Representation Policy Iteration. Sridhar Mahadevan. Department of Computer
Science. University of Massachusetts. 140 Governor's Drive. Amherst, MA 01003.
In N. Lavra, L. Gamberger, and L. Todorovski, editors, Proceedings of the 14th European Conference on Machine Learn- ing, pages 96â107, Dubrovnik, Croatia, ...
high value, low volume spare parts which must be available to respond to ..... taj : total no. of parts with attribute aj Ñthat need replacement under LOS jЮ at time t ..... in Barnhart, C. and Laporte, G. (Eds), Handbooks in Operations Research.
Apr 18, 1996 - iteration (PI) [Howard, 1960]), are compared on a class of problems from the mo- tion planning domain. Policy iteration and its modi cations are ...
Apr 18, 1996 - there are four available actions, North, South, East and West, that .... versions of PI on the motion planning domain, and check if the policy.
Abstract. This paper analyzes asymptotic convergence properties of policy iteration in a class of stationary, infinite-horizon Markovian decision problems that ...
Dec 3, 2015 - of using offline and online methods in tandem as a hybrid ADP procedure, making possi- ...... high λ. Darker shades indicate higher expected rewards. Table 4 .... GoodsonRolloutFramework.pdf, Accessed on June 18, 2015.
Une étude des pathologies d'un ouvrage se décompose en plusieurs phases. ....
Etude des désordres affectant un bâtiment ou un matériau en oeuvre; une ...
many respects as challenging as optimal control of gen- eral nonlinear or hybrid systems. ... dynamic programming [3], the need for approximate solutions was ...
Pathologies of Approximate Policy Iteration in Dynamic ... - MIT
Pathologies of Approximate Policy Iteration in Dynamic. Programming. Dimitri P. Bertsekas. Laboratory for Information and Decision Systems. Massachusetts ...
Pathologies of Approximate Policy Iteration in Dynamic Programming Dimitri P. Bertsekas Laboratory for Information and Decision Systems Massachusetts Institute of Technology
March 2011
Summary We consider policy iteration with cost function approximation Used widely but exhibits very complex behavior and a variety of potential pathologies Case of the tetris test problem Two types of pathologies Deterministic: Due to cost function approximation Stochastic: Due to simulation errors/noise
We survey the pathologies in Policy evaluation: Due to errors in approximate evaluation of policies Policy improvement: Due to policy improvement mechanism
Special focus: Policy oscillations and local attractors Causes of the problem in TD/projected equation methods: The projection operator may not be monotone The projection norm may depend on the policy evaluated
We discuss methods that address the difficulty
References
D. P. Bertsekas, “Pathologies of Temporal Differences Methods in Approximate Dynamic Programming," Proc. 2010 IEEE Conference on Decision and Control, Proc. 2010 IEEE Conference on Decision and Control, Atlanta, GA. D. P. Bertsekas, Dynamic Programming and Optimal Control, Vol. II, 2007, Supplementary Chapter on Approximate DP: On-line; a “living chapter."
MDP: Brief Review J ∗ (i) = Optimal cost starting from state i Jµ (i) = Cost starting from state i using policy µ Denote by T and Tµ the DP mappings that transform J ∈
