  To improve the sample efficiency of policy-gradient based reinforcement
learning algorithms, we propose implicit distributional actor-critic (IDAC)
that consists of a distributional critic, built on two deep generator networks
(DGNs), and a semi-implicit actor (SIA), powered by a flexible policy
distribution. We adopt a distributional perspective on the discounted
cumulative return and model it with a state-action-dependent implicit
distribution, which is approximated by the DGNs that take state-action pairs
and random noises as their input. Moreover, we use the SIA to provide a
semi-implicit policy distribution, which mixes the policy parameters with a
reparameterizable distribution that is not constrained by an analytic density
function. In this way, the policy's marginal distribution is implicit,
providing the potential to model complex properties such as covariance
structure and skewness, but its parameter and entropy can still be estimated.
We incorporate these features with an off-policy algorithm framework to solve
problems with continuous action space and compare IDAC with state-of-the-art
algorithms on representative OpenAI Gym environments. We observe that IDAC
outperforms these baselines in most tasks. Python code is provided.
