Concurrent probabilistic temporal planning with policy-gradients
We present an any-time concurrent probabilistic temporal planner that includes continuous and discrete uncertainties and metric functions. Our approach is a direct policy search that attempts to optimise a parameterised policy using gradient ascent. Low memory use, plus the use of function approximation methods, plus factorisation of the policy, allow us to scale to challenging domains. This Factored Policy Gradient (FPG) Planner also attempts to optimise both steps to goal and the probability...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of The 17th International Conference on Automated Planning and Scheduling (ICAPS 2007)|
|01_Aberdeen_Concurrent_probabilistic_2007.pdf||441.84 kB||Adobe PDF||Request a copy|
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