Deep Conservative Policy Iteration
This work revisits a classic algorithm to potentially enhance stability in deep RL, but it is incremental as it adapts existing methods to new contexts.
The authors tackled the challenge of implementing Conservative Policy Iteration (CPI) with neural networks in deep reinforcement learning, achieving competitive performance on a subset of Atari games and demonstrating improved stability.
Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of consecutive policies. It comes with strong theoretical guarantees, and inspired approaches in deep Reinforcement Learning (RL). However, CPI itself has rarely been implemented, never with neural networks, and only experimented on toy problems. In this paper, we show how CPI can be practically combined with deep RL with discrete actions. We also introduce adaptive mixture rates inspired by the theory. We experiment thoroughly the resulting algorithm on the simple Cartpole problem, and validate the proposed method on a representative subset of Atari games. Overall, this work suggests that revisiting classic ADP may lead to improved and more stable deep RL algorithms.