Support SciPy sparse matrices for alpharank code, implement Stochasically Stable Distributions

open_spiel/python/algorithms/psro_v2/psro_v2.py

@@ -154,8 +154,10 @@ def __init__(self,
 
     # Alpharank is a special case here, as it's not supported by the abstract
     # meta trainer api, so has to be passed as a function instead of a string.
-    if not meta_strategy_method or meta_strategy_method == "alpharank":
+    if meta_strategy_method == "alpharank":
       meta_strategy_method = utils.alpharank_strategy
+    if meta_strategy_method == "ssd":
+      meta_strategy_method = utils.ssd_strategy
 
     print("Sampling from marginals : {}".format(sample_from_marginals))
     self.sample_from_marginals = sample_from_marginals

open_spiel/python/algorithms/psro_v2/utils.py

@@ -22,6 +22,7 @@
 from open_spiel.python.algorithms import policy_aggregator_joint
 from open_spiel.python.egt import alpharank
 from open_spiel.python.egt import utils as alpharank_utils
+from open_spiel.python.egt import ssd
 
 
 def empty_list_generator(number_dimensions):
@@ -221,7 +222,6 @@ def get_joint_strategy_from_marginals(probabilities):
     probas.append(np.array(probabilities[i]).reshape(probas_shapes))
   return np.prod(probas)
 
-
 def alpharank_strategy(solver, return_joint=False, **unused_kwargs):
   """Returns AlphaRank distribution on meta game matrix.
 
@@ -241,12 +241,69 @@ def alpharank_strategy(solver, return_joint=False, **unused_kwargs):
   meta_games = solver.get_meta_game()
   meta_games = [np.asarray(x) for x in meta_games]
 
+  solver_kwargs = solver.get_kwargs()
+  alpharank_kwargs = {}
+  alpharank_kwargs["use_sparse"] = (solver_kwargs["use_sparse"] 
+                                    if "use_sparse" in solver_kwargs 
+                                    else False)
+
   if solver.symmetric_game:
     meta_games = [meta_games[0]]
 
     # Get alpharank distribution via alpha-sweep
     joint_distr = alpharank.sweep_pi_vs_epsilon(
-        meta_games)
+        meta_games, **alpharank_kwargs)
+    joint_distr = remove_epsilon_negative_probs(joint_distr)
+
+    marginals = 2 * [joint_distr]
+    joint_distr = get_joint_strategy_from_marginals(marginals)
+    if return_joint:
+      return marginals, joint_distr
+    else:
+      return joint_distr
+
+  else:
+    joint_distr = alpharank.sweep_pi_vs_epsilon(meta_games,
+                                               **alpharank_kwargs)
+    joint_distr = remove_epsilon_negative_probs(joint_distr)
+
+    if return_joint:
+      marginals = get_alpharank_marginals(meta_games, joint_distr)
+      return marginals, joint_distr
+    else:
+      return joint_distr
+
+def ssd_strategy(solver, return_joint=False, **unused_kwargs):
+  """Returns SSD distribution on meta game matrix.
+
+  Args:
+    solver: GenPSROSolver instance.
+    return_joint: a boolean specifying whether to return player-wise
+      marginals.
+
+  Returns:
+    marginals: a list, specifying for each player the alpharank marginal
+      distributions on their strategies.
+    joint_distr: a list, specifying the joint alpharank distributions for all
+      strategy profiles.
+  """
+  meta_games = solver.get_meta_game()
+  meta_games = [np.asarray(x) for x in meta_games]
+
+  solver_kwargs = solver.get_kwargs() if hasattr(solver, "get_kwargs") else {}
+  ssd_kwargs = {}
+  if isinstance(solver_kwargs, dict):
+    for key, value in solver_kwargs.items():
+      if value is not None:
+        ssd_kwargs[key] = value
+
+  if solver.symmetric_game:
+    meta_games = [meta_games[0]]
+
+    # alpharank distribution via alpha-sweep
+    # joint_distr = alpharank.sweep_pi_vs_epsilon(
+    #     meta_games)
+    joint_distr = ssd.compute_ssd(meta_games, **ssd_kwargs)
     joint_distr = remove_epsilon_negative_probs(joint_distr)
 
     marginals = 2 * [joint_distr]
@@ -257,7 +314,7 @@ def alpharank_strategy(solver, return_joint=False, **unused_kwargs):
       return joint_distr
 
   else:
-    joint_distr = alpharank.sweep_pi_vs_epsilon(meta_games)
+    joint_distr = ssd.compute_ssd(meta_games, **ssd_kwargs)
     joint_distr = remove_epsilon_negative_probs(joint_distr)
 
     if return_joint: