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