Nov 26, 2013 · This policy makes use of win/loss states (WLSs) to learn win rates for large sets of features. A very large experimental series of 7960 games ...

This policy makes use of win/loss states (WLSs) to learn win rates for large sets of features. A very large experimental series of 7960 games includes results ...

This policy makes use of Win/Loss States (WLS) to learn win rates for large sets of features. A very large experimental series of 7,960 games includes ...

Apr 25, 2024 · Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States. IEEE Trans. Comput. Intell. AI Games 6(1): 46-54 ...

Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States. IEEE Trans. Comput. Intell. AI Games 6(1): 46-54 (2014). [+] ...

This paper explores adaptive playout policies which improve the playout policy during a tree search. With the help of policy gradient reinforcement learning ...

Two online learning playout policies in Monte Carlo Go: An application of win/loss states. J Basaldúa, S Stewart, JM Moreno-Vega, PD Drake.

Two online learning playout policies in Monte Carlo Go: An application of win/loss states. J Basaldúa, S Stewart, JM Moreno-Vega, PD Drake. IEEE Transactions ...

78-89. Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States pp. 46-54. General Self-Motivation and Strategy ...

Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States ... two simulation algorithms known as playout policies, the base policy ...