Recursive Least-Squares Temporal Difference With Gradient Correction

Boosting On-Policy Actor-Critic With Shallow Updates in Critic

Deep reinforcement learning (DRL) benefits from the representation power of deep neural networks (NNs), to approximate the value function and policy in the learning process. Batch reinforcement learning (BRL) benefits from stable training and data efficiency with fixed representation and enjoys solid theoretical analysis. This work proposes least-squares deep policy gradient (LSDPG), a hybrid approach that combines least-squares reinforcement learning (RL) with online DRL to achieve the best of both worlds. LSDPG leverages a shared network to share useful features between policy (actor) and value function (critic). LSDPG learns policy, value function, and representation separately. First, LSDPG views deep NNs of the critic as a linear combination of representation weighted by the weights of the last layer and performs policy evaluation with regularized least-squares temporal difference (LSTD) methods. Second, arbitrary policy gradient algorithms can be applied to improve the policy. Third, an auxiliary task is used to periodically distill the features from the critic into the representation. Unlike most DRL methods, where the critic algorithms are often used in a nonstationary situation, i.e., the policy to be evaluated is changing, the critic in LSDPG is working on a stationary case in each iteration of the critic update. We prove that, under some conditions, the critic converges to the regularized TD fixpoint of current policy, and the actor converges to the local optimal policy. The experimental results on challenging Procgen benchmark illustrate the improvement of sample efficiency of LSDPG over proximal policy optimization and phasic policy gradient (PPG).