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We propose a new constrained Markov decision process framework with risk-type constraints. The risk metric we use is Conditional Value-at-Risk (CVaR), which is gaining popularity in finance. It is a conditional expectation but the conditioning is defined in terms of the level of the tail probability. We propose an iterative offline algorithm to find the risk-contrained optimal control policy. A stochastic...
A novel infinite-horizon policy-gradient estimation method with variable discount factor is proposed in this paper. This method tackles the normal policy-gradient estimation methods' limitations on unbalance of the bias and variance by using an incremental sequence as the discount factor. Numerical experiments conducted on the Markov decision process have shown its effectiveness.
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