197 lines
8.0 KiB
Python
197 lines
8.0 KiB
Python
import model
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from qlearningAgents import PacmanQAgent
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from backend import ReplayMemory
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import layout
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import copy
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import torch
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import numpy as np
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import os
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class PacmanDeepQAgent(PacmanQAgent):
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def __init__(self, layout_input="smallGrid", target_update_rate=300, doubleQ=True, **args):
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PacmanQAgent.__init__(self, **args)
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self.model = None
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self.target_model = None
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self.target_update_rate = target_update_rate
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self.update_amount = 0
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self.epsilon_explore = 1.0
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self.epsilon0 = 0.4
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self.minimal_epsilon = 0.01
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if model.kProductionMode:
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self.epsilon_explore=0.01
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self.epsilon0=0.01
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self.minimal_epsilon=0.01
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print("in production mode, epsilon set to 0.01")
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self.epsilon = self.epsilon0
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self.discount = 0.95
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self.update_frequency = 3
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self.counts = None
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self.replay_memory = ReplayMemory(50000)
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self.min_transitions_before_training = 10000
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self.td_error_clipping = 10
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# Initialize Q networks:
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if isinstance(layout_input, str):
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layout_instantiated = layout.getLayout(layout_input)
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else:
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layout_instantiated = layout_input
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self.state_dim = self.get_state_dim(layout_instantiated)
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self.initialize_q_networks(self.state_dim)
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self.doubleQ = doubleQ
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if self.doubleQ:
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self.target_update_rate = -1
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def get_state_dim(self, layout):
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pac_ft_size = 2
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ghost_ft_size = 2 * layout.getNumGhosts()
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food_capsule_ft_size = layout.width * layout.height
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return pac_ft_size + ghost_ft_size + food_capsule_ft_size
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def get_features(self, state):
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pacman_state = np.array(state.getPacmanPosition())
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ghost_state = np.array(state.getGhostPositions())
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capsules = state.getCapsules()
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food_locations = np.array(state.getFood().data).astype(np.float32)
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for x, y in capsules:
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food_locations[x][y] = 2
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return np.concatenate((pacman_state, ghost_state.flatten(), food_locations.flatten()))
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def initialize_q_networks(self, state_dim, action_dim=5):
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import model
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self.model = model.DeepQNetwork(state_dim, action_dim)
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self.target_model = model.DeepQNetwork(state_dim, action_dim)
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if os.path.exists('para.bin'):
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print("Loading model parameters from para.bin")
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checkpoint = torch.load('para.bin')
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self.model.load_state_dict(checkpoint['model_state_dict'])
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self.target_model.load_state_dict(checkpoint['target_model_state_dict'])
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self.model.optimizer.load_state_dict(checkpoint['model_optimizer_state_dict'])
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self.target_model.optimizer.load_state_dict(checkpoint['target_model_optimizer_state_dict'])
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self.replay_memory = checkpoint['memory']
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print(self.model.state_dict())
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else:
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print("Initializing new model parameters")
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def save_model(self, filename="para.bin"):
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if model.kProductionMode:
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print("in production mode, not saving model")
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return
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print(f"Saving model parameters to {filename}")
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torch.save({
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'model_state_dict': self.model.state_dict(),
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'target_model_state_dict': self.target_model.state_dict(),
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'model_optimizer_state_dict': self.model.optimizer.state_dict(),
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"target_model_optimizer_state_dict": self.target_model.optimizer.state_dict(),
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"memory": self.replay_memory
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}, filename)
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print(self.model.state_dict())
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def getQValue(self, state, action):
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"""
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Should return Q(state,action) as predicted by self.model
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"""
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feats = self.get_features(state)
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legalActions = self.getLegalActions(state)
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action_index = legalActions.index(action)
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state = torch.tensor(np.array([feats]).astype("float64"), dtype=torch.double)
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return self.model.run(state).data[0][action_index]
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def shape_reward(self, reward):
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if reward > 100:
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reward = 10
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elif reward > 0 and reward < 10:
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reward = 2
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elif reward == -1:
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reward = 0
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elif reward < -100:
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reward = -10
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return reward
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def compute_q_targets(self, minibatch, network = None, target_network=None, doubleQ=False):
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"""Prepare minibatches
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Args:
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minibatch (List[Transition]): Minibatch of `Transition`
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Returns:
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float: Loss value
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"""
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if network is None:
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network = self.model
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if target_network is None:
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target_network = self.target_model
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states = np.vstack([x.state for x in minibatch])
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states = torch.tensor(states, dtype=torch.double)
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actions = np.array([x.action for x in minibatch])
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rewards = np.array([x.reward for x in minibatch])
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next_states = np.vstack([x.next_state for x in minibatch])
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next_states = torch.tensor(next_states)
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done = np.array([x.done for x in minibatch])
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Q_predict = network.run(states).data.detach().cpu().numpy()
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Q_target = np.copy(Q_predict )
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state_indices = states.int().detach().numpy()
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state_indices = (state_indices[:, 0], state_indices[:, 1])
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exploration_bonus = 1 / (2 * np.sqrt((self.counts[state_indices] / 100)))
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replace_indices = np.arange(actions.shape[0])
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action_indices = np.argmax(network.run(next_states).data.cpu(), axis=1)
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target = rewards + exploration_bonus + (1 - done) * self.discount * target_network.run(next_states).data[replace_indices, action_indices].detach().cpu().numpy()
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Q_target[replace_indices, actions] = target
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if self.td_error_clipping is not None:
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Q_target = Q_predict + np.clip(
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Q_target - Q_predict, -self.td_error_clipping, self.td_error_clipping)
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return Q_target
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def update(self, state, action, nextState, reward):
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legalActions = self.getLegalActions(state)
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action_index = legalActions.index(action)
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done = nextState.isLose() or nextState.isWin()
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reward = self.shape_reward(reward)
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if self.counts is None:
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x, y = np.array(state.getFood().data).shape
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self.counts = np.ones((x, y))
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state = self.get_features(state)
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nextState = self.get_features(nextState)
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self.counts[int(state[0])][int(state[1])] += 1
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transition = (state, action_index, reward, nextState, done)
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self.replay_memory.push(*transition)
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if len(self.replay_memory) < self.min_transitions_before_training:
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self.epsilon = self.epsilon_explore
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else:
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self.epsilon = max(self.epsilon0 * (1 - self.update_amount / 20000), self.minimal_epsilon)
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if len(self.replay_memory) > self.min_transitions_before_training and self.update_amount % self.update_frequency == 0:
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minibatch = self.replay_memory.pop(self.model.batch_size)
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states = np.vstack([x.state for x in minibatch])
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states = torch.tensor(states.astype("float64"), dtype=torch.double)
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Q_target1 = self.compute_q_targets(minibatch, self.model, self.target_model, doubleQ=self.doubleQ)
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Q_target1 = torch.tensor(Q_target1.astype("float64"), dtype=torch.double)
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if self.doubleQ:
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Q_target2 = self.compute_q_targets(minibatch, self.target_model, self.model, doubleQ=self.doubleQ)
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Q_target2 = torch.tensor(Q_target2.astype("float64"), dtype=torch.double)
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self.model.gradient_update(states, Q_target1)
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if self.doubleQ:
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self.target_model.gradient_update(states, Q_target2)
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if self.target_update_rate > 0 and self.update_amount % self.target_update_rate == 0:
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self.target_model.set_weights(copy.deepcopy(self.model.parameters))
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self.update_amount += 1
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def final(self, state):
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"""Called at the end of each game."""
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PacmanQAgent.final(self, state)
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