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.
The proposed approach is related to actor-critics in general, being itself an actor-critic. It is notably related to TRPO @cite_23 , that introduced a KL penalty on the greedy step as an alternative to the stochastic mixture of CPI. This is indeed very useful for continuous actions, but probably unnecessary for discrete actions, the case considered here. Moreover, TRPO is an on-policy algorithm, while the proposed DCPI approach is off-policy. The principle of regularizing greediness in actor-critics is quite widespread, be it with a KL divergence constraint (TRPO), a clipping of policies ratio (PPO @cite_18 ), entropy regularization (SAC), or even following policy gradient, for example. The common point of these approaches is that they focus on continuous action spaces. In the discrete case, considering a stochastic mixture is quite natural, acknowledging that its extension to the continuous case is not straightforward.
@cite_18 @cite_23
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In this work, we show that discretizing action space for continuous control is a simple yet powerful technique for on-policy optimization. The explosion in the number of discrete actions can be efficiently addressed by a policy with factorized distribution across action dimensions. We show that the discrete policy achieves significant performance gains with state-of-the-art on-policy optimization algorithms (PPO, TRPO, ACKTR) especially on high-dimensional tasks with complex dynamics. Additionally, we show that an ordinal parameterization of the discrete distribution can introduce the inductive bias that encodes the natural ordering between discrete actions. This ordinal architecture further significantly improves the performance of PPO TRPO. In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also a method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2- to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at this https URL In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also the method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2- to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at https: github.com openai baselines We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policiesfor Partially Observable Markov Decision Processes (POMDPs) that require long-term memories of past observations and actions. The approach involves estimating a policy gradient for an Actor through a Policy Gradient Critic which evaluates probability distributions on actions. Gradient-based updates of history-conditional action probability distributions enable the algorithm to learn a mapping from memory states (or event histories) to probability distributions on actions, solving POMDPs through a combination of memory and stochasticity. This goes beyond previous approaches to learning purely reactive POMDP policies, without giving up their advantages. Preliminary results on important benchmark tasks show that our approach can in principle be used as a general purpose POMDP algorithm that solves RL problems in both continuous and discrete action domains.