  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.
