Unify num players and actions

Former-commit-id: aac122c

examples/human/blackjack_human.py

@@ -9,10 +9,10 @@
 
 # Make environment and enable human mode
 # Set 'record_action' to True because we need it to print results
-player_num = 2
-env = rlcard.make('blackjack', config={'record_action': True, 'game_player_num': player_num})
-human_agent = HumanAgent(env.action_num)
-random_agent = RandomAgent(env.action_num)
+num_players = 2
+env = rlcard.make('blackjack', config={'record_action': True, 'game_num_players': num_players})
+human_agent = HumanAgent(env.num_actions)
+random_agent = RandomAgent(env.num_actions)
 env.set_agents([human_agent, random_agent])
 
 print(">> Blackjack human agent")
@@ -30,7 +30,7 @@
         state = []
         _action_list = []
 
-        for i in range(player_num):
+        for i in range(num_players):
             final_state.append(trajectories[i][-1])
             state.append(final_state[i]['raw_obs'])
 
@@ -45,12 +45,12 @@
     print('===============   Dealer hand   ===============')
     print_card(state[0]['state'][1])
 
-    for i in range(player_num):
+    for i in range(num_players):
         print('===============   Player {} Hand   ==============='.format(i))
         print_card(state[i]['state'][0])
 
     print('===============     Result     ===============')
-    for i in range(player_num):
+    for i in range(num_players):
         if payoffs[i] == 1:
             print('Player {} win {} chip!'.format(i, payoffs[i]))
         elif payoffs[i] == 0:

examples/human/gin_rummy_human.py

@@ -39,9 +39,9 @@
 
 def make_gin_rummy_env() -> 'GinRummyEnv':
     gin_rummy_env = rlcard.make('gin-rummy')
-    # north_agent = RandomAgent(action_num=gin_rummy_env.action_num)
+    # north_agent = RandomAgent(num_actions=gin_rummy_env.num_actions)
     north_agent = GinRummyNoviceRuleAgent()
-    south_agent = HumanAgent(gin_rummy_env.action_num)
+    south_agent = HumanAgent(gin_rummy_env.num_actions)
     gin_rummy_env.set_agents([north_agent, south_agent])
     gin_rummy_env.game.judge.scorer = scorers.GinRummyScorer(get_payoff=scorers.get_payoff_gin_rummy_v0)
     return gin_rummy_env

examples/human/leduc_holdem_human.py

@@ -9,7 +9,7 @@
 # Make environment
 # Set 'record_action' to True because we need it to print results
 env = rlcard.make('leduc-holdem', config={'record_action': True})
-human_agent = HumanAgent(env.action_num)
+human_agent = HumanAgent(env.num_actions)
 cfr_agent = models.load('leduc-holdem-cfr').agents[0]
 env.set_agents([human_agent, cfr_agent])
 

examples/human/limit_holdem_human.py

@@ -9,8 +9,8 @@
 # Make environment and enable human mode
 # Set 'record_action' to True because we need it to print results
 env = rlcard.make('limit-holdem', config={'record_action': True})
-human_agent = HumanAgent(env.action_num)
-agent_0 = RandomAgent(action_num=env.action_num)
+human_agent = HumanAgent(env.num_actions)
+agent_0 = RandomAgent(num_actions=env.num_actions)
 env.set_agents([human_agent, agent_0])
 
 print(">> Limit Hold'em random agent")

examples/human/nolimit_holdem_human.py

@@ -11,9 +11,9 @@
 # Set 'record_action' to True because we need it to print results
 env = rlcard.make('no-limit-holdem', config={'record_action': True})
 
-human_agent = HumanAgent(env.action_num)
-human_agent2 = HumanAgent(env.action_num)
-# random_agent = RandomAgent(action_num=env.action_num)
+human_agent = HumanAgent(env.num_actions)
+human_agent2 = HumanAgent(env.num_actions)
+# random_agent = RandomAgent(num_actions=env.num_actions)
 
 env.set_agents([human_agent, human_agent2])
 

examples/human/uno_human.py

@@ -8,7 +8,7 @@
 # Make environment and enable human mode
 # Set 'record_action' to True because we need it to print results
 env = rlcard.make('uno', config={'record_action': True})
-human_agent = HumanAgent(env.action_num)
+human_agent = HumanAgent(env.num_actions)
 cfr_agent = models.load('uno-rule-v1').agents[0]
 env.set_agents([human_agent, cfr_agent])
 

rlcard/agents/dqn_agent.py

@@ -236,7 +236,7 @@ def __init__(self, num_actions=2, learning_rate=0.001, state_shape=None, mlp_lay
         ''' Initilalize an Estimator object.
 
         Args:
-            action_num (int): the number output actions
+            num_actions (int): the number output actions
             state_shape (list): the shape of the state space
             mlp_layers (list): size of outputs of mlp layers
             device (torch.device): whether to use cpu or gpu
@@ -303,10 +303,10 @@ def update(self, s, a, y):
         a = torch.from_numpy(a).long().to(self.device)
         y = torch.from_numpy(y).float().to(self.device)
 
-        # (batch, state_shape) -> (batch, action_num)
+        # (batch, state_shape) -> (batch, num_actions)
         q_as = self.qnet(s)
 
-        # (batch, action_num) -> (batch, )
+        # (batch, num_actions) -> (batch, )
         Q = torch.gather(q_as, dim=-1, index=a.unsqueeze(-1)).squeeze(-1)
 
         # update model

rlcard/agents/human_agents/gin_rummy_human_agent/gui_gin_rummy/Gin-Rummy-GUI-Design.md

@@ -166,9 +166,9 @@ The example uses the following:
 ```python
 def make_gin_rummy_env() -> 'GinRummyEnv':
     gin_rummy_env = rlcard.make('gin-rummy')
-    # north_agent = RandomAgent(action_num=gin_rummy_env.action_num)
+    # north_agent = RandomAgent(num_actions=gin_rummy_env.num_actions)
     north_agent = GinRummyNoviceRuleAgent()
-    south_agent = HumanAgent(gin_rummy_env.action_num)
+    south_agent = HumanAgent(gin_rummy_env.num_actions)
     gin_rummy_env.set_agents([north_agent, south_agent])
     return gin_rummy_env
 ```

rlcard/agents/human_agents/gin_rummy_human_agent/gui_gin_rummy/game_app.py

@@ -37,7 +37,7 @@ def __init__(self, make_gin_rummy_env: Callable[[], 'GinRummyEnv'] = None):
     @staticmethod
     def _make_gin_rummy_env() -> 'GinRummyEnv':
         gin_rummy_env = rlcard.make('gin-rummy')
-        north_agent = RandomAgent(action_num=gin_rummy_env.action_num)
-        south_agent = HumanAgent(gin_rummy_env.action_num)
+        north_agent = RandomAgent(num_actions=gin_rummy_env.num_actions)
+        south_agent = HumanAgent(gin_rummy_env.num_actions)
         gin_rummy_env.set_agents([north_agent, south_agent])
         return gin_rummy_env

rlcard/agents/nfsp_agent.py

@@ -265,10 +265,10 @@ def train_sl(self):
         # (batch, state_size)
         info_states = torch.from_numpy(np.array(info_states)).float().to(self.device)
 
-        # (batch, action_num)
+        # (batch, num_actions)
         eval_action_probs = torch.from_numpy(np.array(action_probs)).float().to(self.device)
 
-        # (batch, action_num)
+        # (batch, num_actions)
         log_forecast_action_probs = self.policy_network(info_states)
 
         ce_loss = - (eval_action_probs * log_forecast_action_probs).sum(dim=-1).mean()

rlcard/agents/random_agent.py

@@ -9,7 +9,7 @@ def __init__(self, num_actions):
         ''' Initilize the random agent
 
         Args:
-            action_num (int): The size of the ouput action space
+            num_actions (int): The size of the ouput action space
         '''
         self.use_raw = False
         self.num_actions = num_actions

rlcard/envs/env.py

@@ -17,7 +17,7 @@ def __init__(self, config):
                  step_back.
                 There can be some game specific configurations, e.g., the
                 number of players in the game. These fields should start with
-                'game_', e.g., 'game_player_num' which specify the number of
+                'game_', e.g., 'game_num_players' which specify the number of
                 players in the game. Since these configurations may be game-specific,
                 The default settings should be put in the Env class. For example,
                 the default game configurations for Blackjack should be in

tests/games/test_doudizhu_game.py

@@ -10,7 +10,7 @@
 
 class TestDoudizhuGame(unittest.TestCase):
 
-    def test_get_player_num(self):
+    def test_get_num_players(self):
         game = Game()
         num_players = game.get_num_players()
         self.assertEqual(num_players, 3)

tests/games/test_limitholdem_game.py

@@ -10,7 +10,7 @@ class TestLimitholdemMethods(unittest.TestCase):
     def test_get_num_actions(self):
         game = Game()
         num_players = game.get_num_players()
-        self.assertEqual(player_num, 2)
+        self.assertEqual(num_players, 2)
 
     def test_get_num_actions(self):
         game = Game()