A particle-based policy for the optimal control of Markov decision processes

When the state dimension is large, classical approximate dynamic programming techniques may become computationally unfeasible, since the complexity of the algorithm grows exponentially with the state space size (curse of dimensionality). Policy search techniques are able to overcome this problem because, instead of estimating the value function over the entire state space, they search for the optimal control policy in a restricted parameterized policy space. This paper presents a new policy parametrization that exploits a single point (particle) to represent an entire region of the state space and can be tuned through a recently introduced policy gradient method with parameter-based exploration. Experiments demonstrate the superior performance of the proposed approach in high dimensional environments.