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Learning Policies through Quantile Regression
Policy gradient based reinforcement learning algorithms coupled with neural
networks have shown success in learning complex policies in the model free
continuous action space control setting. However, explicitly parameterized
policies are limited by the scope of the chosen parametric probability
distribution. We show that alternatively to the likelihood based policy
gradient, a related objective can be optimized through advantage weighted
quantile regression. Our approach models the policy implicitly in the network,
which gives the agent the freedom to approximate any distribution in each
action dimension, not limiting its capabilities to the commonly used unimodal
Gaussian parameterization. This broader spectrum of policies makes our
algorithm suitable for problems where Gaussian policies cannot fit the optimal
policy. Moreover, our results on the MuJoCo physics simulator benchmarks are
comparable or superior to state-of-the-art on-policy methods.Comment: Preprin