Mixed Policy Gradient

Reinforcement learning (RL) has great potential in sequential decision-making. At present, the mainstream RL algorithms are data-driven, relying on millions of iterations and a large number of empirical data to learn a policy. Although data-driven RL may have excellent asymptotic performance, it usually yields slow convergence speed. As a comparison, model-driven RL employs a differentiable transition model to improve convergence speed, in which the policy gradient (PG) is calculated by using the backpropagation through time (BPTT) technique. However, such methods suffer from numerical instability, model error sensitivity and low computing efficiency, which may lead to poor policies. In this paper, a mixed policy gradient (MPG) method is proposed, which uses both empirical data and the transition model to construct the PG, so as to accelerate the convergence speed without losing the optimality guarantee. MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return. We unify them by revealing the correlation between the upper bound of the unified PG error and the predictive horizon, where the data-driven PG is regraded as 0-step model-predictive return. Relying on that, MPG employs a rule-based method to adaptively adjust the weights of data-driven and model-driven PGs. In particular, to get a more accurate PG, the weight of the data-driven PG is designed to grow along the learning process while the other to decrease. Besides, an asynchronous learning framework is proposed to reduce the wall-clock time needed for each update iteration. Simulation results show that the MPG method achieves the best asymptotic performance and convergence speed compared with other baseline algorithms.