Boosting the Actor with Dual Critic

Anonymous

Nov 07, 2017 (modified: Nov 07, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: This paper proposes a new actor-crtitic-style algorithm called Dual Actor-Critic or Dual-AC . It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for the dual critic learning that is directly related to the objective function of the actor. We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm. We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.
  • TL;DR: We propose Dual Actor-Critic algorithm, which is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation. The algorithm achieves the state-of-the-art performances across several benchmarks.
  • Keywords: reinforcement learning, actor-critic algorithm, Lagrangian duality

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