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deepQLearningAgents.py
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import nn
import model
from qlearningAgents import PacmanQAgent
from backend import ReplayMemory
import layout
import copy
import numpy as np
class PacmanDeepQAgent(PacmanQAgent):
def __init__(self, layout_input="smallGrid", target_update_rate=300, doubleQ=True, **args):
PacmanQAgent.__init__(self, **args)
self.model = None
self.target_model = None
self.target_update_rate = target_update_rate
self.update_amount = 0
self.epsilon_explore = 1.0
self.epsilon0 = 0.05
self.epsilon = self.epsilon0
self.discount = 0.9
self.update_frequency = 3
self.counts = None
self.replay_memory = ReplayMemory(50000)
self.min_transitions_before_training = 10000
self.td_error_clipping = 1
# Initialize Q networks:
if isinstance(layout_input, str):
layout_instantiated = layout.getLayout(layout_input)
else:
layout_instantiated = layout_input
self.state_dim = self.get_state_dim(layout_instantiated)
self.initialize_q_networks(self.state_dim)
self.doubleQ = doubleQ
if self.doubleQ:
self.target_update_rate = -1
def get_state_dim(self, layout):
pac_ft_size = 2
ghost_ft_size = 2 * layout.getNumGhosts()
food_capsule_ft_size = layout.width * layout.height
return pac_ft_size + ghost_ft_size + food_capsule_ft_size
def get_features(self, state):
pacman_state = np.array(state.getPacmanPosition())
ghost_state = np.array(state.getGhostPositions())
capsules = state.getCapsules()
food_locations = np.array(state.getFood().data).astype(np.float32)
for x, y in capsules:
food_locations[x][y] = 2
return np.concatenate((pacman_state, ghost_state.flatten(), food_locations.flatten()))
def initialize_q_networks(self, state_dim, action_dim=5):
import model
self.model = model.DeepQNetwork(state_dim, action_dim)
self.target_model = model.DeepQNetwork(state_dim, action_dim)
def getQValue(self, state, action):
"""
Should return Q(state,action) as predicted by self.model
"""
feats = self.get_features(state)
legalActions = self.getLegalActions(state)
action_index = legalActions.index(action)
state = nn.Constant(np.array([feats]).astype("float64"))
return self.model.run(state).data[0][action_index]
def shape_reward(self, reward):
if reward > 100:
reward = 10
elif reward > 0 and reward < 10:
reward = 2
elif reward == -1:
reward = 0
elif reward < -100:
reward = -10
return reward
def compute_q_targets(self, minibatch, network = None, target_network=None, doubleQ=False):
"""Prepare minibatches
Args:
minibatch (List[Transition]): Minibatch of `Transition`
Returns:
float: Loss value
"""
if network is None:
network = self.model
if target_network is None:
target_network = self.target_model
states = np.vstack([x.state for x in minibatch])
states = nn.Constant(states)
actions = np.array([x.action for x in minibatch])
rewards = np.array([x.reward for x in minibatch])
next_states = np.vstack([x.next_state for x in minibatch])
next_states = nn.Constant(next_states)
done = np.array([x.done for x in minibatch])
Q_predict = network.run(states).data
Q_target = np.copy(Q_predict)
state_indices = states.data.astype(int)
state_indices = (state_indices[:, 0], state_indices[:, 1])
exploration_bonus = 1 / (2 * np.sqrt((self.counts[state_indices] / 100)))
replace_indices = np.arange(actions.shape[0])
action_indices = np.argmax(network.run(next_states).data, axis=1)
target = rewards + exploration_bonus + (1 - done) * self.discount * target_network.run(next_states).data[replace_indices, action_indices]
Q_target[replace_indices, actions] = target
if self.td_error_clipping is not None:
Q_target = Q_predict + np.clip(
Q_target - Q_predict, -self.td_error_clipping, self.td_error_clipping)
return Q_target
def update(self, state, action, nextState, reward):
legalActions = self.getLegalActions(state)
action_index = legalActions.index(action)
done = nextState.isLose() or nextState.isWin()
reward = self.shape_reward(reward)
if self.counts is None:
x, y = np.array(state.getFood().data).shape
self.counts = np.ones((x, y))
state = self.get_features(state)
nextState = self.get_features(nextState)
self.counts[int(state[0])][int(state[1])] += 1
transition = (state, action_index, reward, nextState, done)
self.replay_memory.push(*transition)
if len(self.replay_memory) < self.min_transitions_before_training:
self.epsilon = self.epsilon_explore
else:
self.epsilon = max(self.epsilon0 * (1 - self.update_amount / 20000), 0)
if len(self.replay_memory) > self.min_transitions_before_training and self.update_amount % self.update_frequency == 0:
minibatch = self.replay_memory.pop(self.model.batch_size)
states = np.vstack([x.state for x in minibatch])
states = nn.Constant(states.astype("float64"))
Q_target1 = self.compute_q_targets(minibatch, self.model, self.target_model, doubleQ=self.doubleQ)
Q_target1 = nn.Constant(Q_target1.astype("float64"))
if self.doubleQ:
Q_target2 = self.compute_q_targets(minibatch, self.target_model, self.model, doubleQ=self.doubleQ)
Q_target2 = nn.Constant(Q_target2.astype("float64"))
self.model.gradient_update(states, Q_target1)
if self.doubleQ:
self.target_model.gradient_update(states, Q_target2)
if self.target_update_rate > 0 and self.update_amount % self.target_update_rate == 0:
self.target_model.set_weights(copy.deepcopy(self.model.parameters))
self.update_amount += 1
def final(self, state):
"""Called at the end of each game."""
PacmanQAgent.final(self, state)