Refinement

.gitignore

@@ -25,4 +25,4 @@ yarn-error.log*
 db.sqlite3
 __pycache__
 *.swp
-/uploaded_agents
+uploaded_agents

server/media/uploaded_agents/example_model.py

@@ -1,84 +0,0 @@
-''' Leduc Hold 'em rule model
-'''
-import rlcard
-from rlcard.models.model import Model
-
-class LeducHoldemRuleAgentV2(object):
-    ''' Leduc Hold 'em Rule agent version 2
-    '''
-    def __init__(self):
-        self.use_raw = True
-
-    def step(self, state):
-        ''' Predict the action when given raw state. A simple rule-based AI.
-        Args:
-            state (dict): Raw state from the game
-
-        Returns:
-            action (str): Predicted action
-        '''
-        legal_actions = state['raw_legal_actions']
-        state = state['raw_obs']
-        hand = state['hand']
-        public_card = state['public_card']
-        action = 'fold'
-        '''
-        When having only 2 hand cards at the game start, choose fold to drop terrible cards:
-        Acceptable hand cards:
-        Pairs
-        AK, AQ, AJ, AT
-        A9s, A8s, ... A2s(s means flush)
-        KQ, KJ, QJ, JT
-        Fold all hand types except those mentioned above to save money
-        '''
-        if public_card:
-            if public_card[1] == hand[1]:
-                action = 'raise'
-            else:
-                action = 'fold'
-        else:
-            if hand[0] == 'K':
-                action = 'raise'
-            elif hand[0] == 'Q':
-                action = 'check'
-            else:
-                action = 'fold'
-
-        #return action
-        if action in legal_actions:
-            return action
-        else:
-            if action == 'raise':
-                return 'call'
-            if action == 'check':
-                return 'fold'
-            if action == 'call':
-                return 'raise'
-            else:
-                return action
-
-    def eval_step(self, state):
-        return self.step(state), []
-
-class LeducHoldemRuleModelV2(Model):
-    ''' Leduc holdem Rule Model version 2
-    '''
-
-    def __init__(self):
-        ''' Load pretrained model
-        '''
-        env = rlcard.make('leduc-holdem')
-        rule_agent = LeducHoldemRuleAgentV2()
-        self.rule_agents = [rule_agent for _ in range(env.player_num)]
-
-    @property
-    def agents(self):
-        ''' Get a list of agents for each position in a the game
-
-        Returns:
-            agents (list): A list of agents
-
-        Note: Each agent should be just like RL agent with step and eval_step
-              functioning well.
-        '''
-        return self.rule_agents

server/tournament/views.py

@@ -16,7 +16,7 @@ from .models import Game, Payoff, UploadedAgent
 
 from .tournament import Tournament
 
-def reset_model_ids():
+def _reset_model_ids():
     from .rlcard_wrap import rlcard, MODEL_IDS
     agents = UploadedAgent.objects.all()
     for agent in agents:
@@ -69,8 +69,12 @@ def query_payoff(request):
 @transaction.atomic
 def launch(request):
     if request.method == 'GET':
+        try:
             eval_num = int(request.GET['eval_num'])
             game = request.GET['name']
+        except:
+            return HttpResponse(json.dumps({'value': -1, 'info': 'parameters error'}))
+
         games_data, payoffs_data = Tournament(game, MODEL_IDS[game], eval_num).launch()
         Game.objects.filter(name=game).delete()
         Payoff.objects.filter(name=game).delete()
@@ -103,7 +107,7 @@ def upload_agent(request):
 
         a = UploadedAgent(name=name, game=game, f=f, entry=entry)
         a.save()
-        reset_model_ids()
+        _reset_model_ids()
         return HttpResponse(json.dumps({'value': 0, 'info': 'success'}))
 
 def delete_agent(request):
@@ -113,7 +117,7 @@ def delete_agent(request):
             return HttpResponse(json.dumps({'value': -1, 'info': 'name not exists'}))
 
         UploadedAgent.objects.filter(name=name).delete()
-        reset_model_ids()
+        _reset_model_ids()
         return HttpResponse(json.dumps({'value': 0, 'info': 'success'}))
 
 def list_agents(request):