Implicit Distributional Reinforcement Learning
This work addresses sample efficiency for reinforcement learning practitioners, but it appears incremental as it builds on existing off-policy frameworks with distributional methods.
The authors tackled the problem of sample inefficiency in policy-gradient reinforcement learning by proposing Implicit Distributional Actor-Critic (IDAC), which uses deep generator networks and a semi-implicit actor to model distributions, and they observed that IDAC outperformed state-of-the-art algorithms on most OpenAI Gym tasks.
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.