  Many continuous control tasks have bounded action spaces. When policy
gradient methods are applied to such tasks, out-of-bound actions need to be
clipped before execution, while policies are usually optimized as if the
actions are not clipped. We propose a policy gradient estimator that exploits
the knowledge of actions being clipped to reduce the variance in estimation. We
prove that our estimator, named clipped action policy gradient (CAPG), is
unbiased and achieves lower variance than the conventional estimator that
ignores action bounds. Experimental results demonstrate that CAPG generally
outperforms the conventional estimator, indicating that it is a better policy
gradient estimator for continuous control tasks. The source code is available
at https://github.com/pfnet-research/capg.

Machine Learning;Artificial Intelligence;Machine Learning