  Learning how to adapt and make real-time informed decisions in dynamic and
complex environments is a challenging problem. To learn this task,
Reinforcement Learning (RL) relies on an agent interacting with an environment
and learning through trial and error to maximize the cumulative sum of rewards
received by it. In multi-player Monopoly game, players have to make several
decisions every turn which involves complex actions, such as making trades.
This makes the decision-making harder and thus, introduces a highly complicated
task for an RL agent to play and learn its winning strategies. In this paper,
we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of
playing and learning winning strategies of the popular board game, Monopoly. To
achieve this, our DRL agent (1) starts its learning process by imitating a
rule-based agent (that resembles the human logic) to initialize its policy, (2)
learns the successful actions, and improves its policy using DRL. Experimental
results demonstrate an intelligent behavior of our proposed agent as it shows
high win rates against different types of agent-players.
