  Reinforcement Learning has recently surfaced as a very powerful tool to solve
complex problems in the domain of board games, wherein an agent is generally
required to learn complex strategies and moves based on its own experiences and
rewards received. While RL has outperformed existing state-of-the-art methods
used for playing simple video games and popular board games, it is yet to
demonstrate its capability on ancient games. Here, we solve one such problem,
where we train our agents using different methods namely Monte Carlo, Qlearning
and Expected Sarsa to learn optimal policy to play the strategic Royal Game of
Ur. The state space for our game is complex and large, but our agents show
promising results at playing the game and learning important strategic moves.
Although it is hard to conclude that when trained with limited resources which
algorithm performs better overall, but Expected Sarsa shows promising results
when it comes to fastest learning.
