  Grant-free random access (RA) techniques are suitable for machine-type
communication (MTC) networks but they need to be adaptive to the MTC traffic,
which is different from the human-type communication. Conventional RA protocols
such as exponential backoff (EB) schemes for slotted-ALOHA suffer from a high
number of collisions and they are not directly applicable to the MTC traffic
models. In this work, we propose to use multi-agent deep Q-network (DQN) with
parameter sharing to find a single policy applied to all machine-type devices
(MTDs) in the network to resolve collisions. Moreover, we consider binary
broadcast feedback common to all devices to reduce signalling overhead. We
compare the performance of our proposed DQN-RA scheme with EB schemes for up to
500 MTDs and show that the proposed scheme outperforms EB policies and provides
a better balance between throughput, delay and collision rate
