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Variable risk control via stochastic optimization
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DOI：10.1177/0278364913476124.png)
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Abstract
En 中文
We present new global and local policy search algorithms suitable for problems with policy-dependent cost variance (or risk), a property present in many robot control tasks. These algorithms exploit new techniques in non-parametric heteroscedastic regression to directly model the policy-dependent distribution of cost. For local search, the learned cost model can be used as a critic for performing risk-sensitive gradient descent. Alternatively, decision-theoretic criteria can be applied to globally select policies to balance exploration and exploitation in a principled way, or to perform greedy minimization with respect to various risk-sensitive criteria. This separation of learning and policy selection permits variable risk control, where risk-sensitivity can be flexibly adjusted and appropriate policies can be selected at runtime without relearning. We describe experiments in dynamic stabilization and manipulation with a mobile manipulator that demonstrate learning of flexible, risk-sensitive policies in very few trials.
Keywords:
policy search
Bayesian optimization
robot learning
risk-sensitive
dynamic mobile manipulation
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