nash.learning package¶
Submodules¶
nashpy.learning.fictitious_play module¶
Code to carry out fictitious learning
- nashpy.learning.fictitious_play.fictitious_play(A, B, iterations, play_counts=None)[source]¶
Implement fictitious play
- Parameters
A (array) – The row player payoff matrix.
B (array) – The column player payoff matrix.
iterations (int) – The number of iterations of the algorithm.
play_counts (array) – The play counts.
- Yields
array – The play counts.
- nashpy.learning.fictitious_play.get_best_response_to_play_count(A, play_count)[source]¶
Returns the best response to a belief based on the playing distribution of the opponent
- Parameters
A (array) – The utility matrix.
play_count (array) – The play counts.
- Returns
The action that corresponds to the best response.
- Return type
int