  The adaptive traffic signal control (ATSC) problem can be modeled as a
multiagent cooperative game among urban intersections, where intersections
cooperate to optimize their common goal. Recently, reinforcement learning (RL)
has achieved marked successes in managing sequential decision making problems,
which motivates us to apply RL in the ASTC problem. Here we use independent
reinforcement learning (IRL) to solve a complex traffic cooperative control
problem in this study. One of the largest challenges of this problem is that
the observation information of intersection is typically partially observable,
which limits the learning performance of IRL algorithms. To this, we model the
traffic control problem as a partially observable weak cooperative traffic
model (PO-WCTM) to optimize the overall traffic situation of a group of
intersections. Different from a traditional IRL task that averages the returns
of all agents in fully cooperative games, the learning goal of each
intersection in PO-WCTM is to reduce the cooperative difficulty of learning,
which is also consistent with the traffic environment hypothesis. We also
propose an IRL algorithm called Cooperative Important Lenient Double DQN
(CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the
forgetful experience mechanism and the lenient weight training mechanism. The
former mechanism decreases the importance of experiences stored in the
experience reply buffer, which deals with the problem of experience failure
caused by the strategy change of other agents. The latter mechanism increases
the weight experiences with high estimation and `leniently' trains the DDQN
neural network, which improves the probability of the selection of cooperative
joint strategies. Experimental results show that CIL-DDQN outperforms other
methods in almost all performance indicators of the traffic control problem.
