def policy_found(q, steps):
from rl.environment import Environment
from rl.agent import Agent
from rl.stateaction import StateAction
environment = Environment()
agent = Agent(environment, Util.get_state_actions, q, 1, 1)
maxStepsAllowed = Util.num_cols + Util.num_rows
stepsToGoal = 0
while stepsToGoal < maxStepsAllowed:
stepsToGoal += 1
prevState = agent.get_state()
agent.test()
action = agent.get_action()
if prevState != Util.MIN_VALUE:
steps.append(StateAction(prevState, action))
if agent.get_state() == Util.get_goal_state():
return True
if agent.terminal:
return False
return agent.get_state() == Util.get_goal_state()
processor=processor, warmup_steps=50000, gamma=.99, target_model_update=10000,
train_interval=4, delta_clip=1.)
# learning rate
#
dqn.compile(Adam(lr=.00025), metrics=['mae'])
#=== TRAIN ===#
if args.mode == 'train':
checkpoint_weights_filename = 'weights_{step}.h5f'
log_filename = 'dqn_log.json'
callbacks = [ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)]
callbacks += [FileLogger(log_filename, interval=100)]
dqn.fit(env, callbacks=callbacks, steps=1750000, log_interval=10000)
# After training is done, we save the final weights
dqn.save_weights('final_weights.h5f', overwrite=True)
#=== TEST ===#
elif args.mode == 'test':
dqn.load_weights('trained_data/final_weights.h5f')
dqn.test(env, episodes=10, visualize=True)