DNA: Proximal Policy Optimization with a Dual Network Architecture
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This paper explores the problem of simultaneously learning a value function and policy in deep actor-critic reinforcement learning models. We find that the common practice of learning these functions jointly is sub-optimal, due to an order-of-magnitude difference in noise levels between these two tasks. Instead, we show that learning these tasks independently, but with a constrained distillation phase, significantly improves performance. Furthermore, we find that the policy gradient noise levels can be decreased by using a lower variance return estimate. Whereas, the value learning noise level decreases with a lower bias estimate. Together these insights inform an extension to Proximal Policy Optimization we call Dual Network Architecture (DNA), which significantly outperforms its predecessor. DNA also exceeds the performance of the popular Rainbow DQN algorithm on four of the five environments tested, even under more difficult stochastic control settings.
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