def train_maze(output_path):
config = Config()
config.set_paths(output_path)
env = EnvMaze(n=config.maze_size, hard=config.hard)
# exploration strategy
exp_schedule = LinearExploration(env, config.eps_begin,
config.eps_end, config.eps_nsteps, config.env_name)
# learning rate schedule
lr_schedule = LinearSchedule(config.lr_begin, config.lr_end,
config.lr_nsteps, config.env_name)
# train model
print(config.output_path)
model = NatureQN(env, config)
model.bfs_len = env.get_bfs_length()
evaluation_result_list, oos_evalution_result_list = model.run(exp_schedule, lr_schedule)
return evaluation_result_list, oos_evalution_result_list
elif FLAGS.network_type == 'deep_q':
network = network.DeepQ(FLAGS)
elif FLAGS.network_type == 'recurrent_q':
network = network.RecurrentQ(FLAGS)
elif FLAGS.network_type == 'transfer_q':
network = network.TransferQ(FLAGS)
elif FLAGS.network_type == 'deep_ac':
network = network.DeepAC(FLAGS)
else: raise NotImplementedError
# Initialize exploration strategy
exp_schedule = LinearExploration(env, FLAGS.epsilon, FLAGS.eps_end, FLAGS.eps_nsteps)
# Initialize exploration rate schedule
lr_schedule = LinearSchedule(FLAGS.learning_rate, FLAGS.lr_end, FLAGS.lr_nsteps)
# train model
model = None
if FLAGS.model_type == 'q':
model = Model(env, record_env, network, FLAGS)
elif FLAGS.model_type == 'ac':
model = ModelAC(env, record_env, network, FLAGS)
else:
raise NotImplementedError
if FLAGS.record_only:
model.record_videos(FLAGS.model_path+'checkpoint')
You'll find the results, log and video recordings of your agent every 250k under
the corresponding file in the results folder. A good way to monitor the progress
of the training is to use Tensorboard. The starter code writes summaries of different
variables.
To launch tensorboard, open a Terminal window and run
tensorboard --logdir=results/
Then, connect remotely to
address-ip-of-the-server:6006
6006 is the default port used by tensorboard.
"""
if __name__ == '__main__':
# make env
env = gym.make(config.env_name)
env = MaxAndSkipEnv(env, skip=config.skip_frame)
env = PreproWrapper(env,
prepro=greyscale,
shape=(80, 80, 1),
overwrite_render=config.overwrite_render)
# exploration strategy
exp_schedule = LinearExploration(env, config.eps_begin, config.eps_end,
config.eps_nsteps)
# learning rate schedule
lr_schedule = LinearSchedule(config.lr_begin, config.lr_end,
config.lr_nsteps)
# train model
model = NatureQN(env, config)
model.run(exp_schedule, lr_schedule)
student_config.lr_nsteps = args.nsteps_train / 2
student_config.exp_policy = args.exp_policy
# make env
env = gym.make(student_config.env_name)
if hasattr(student_config, 'skip_frame'):
env = MaxAndSkipEnv(env, skip=student_config.skip_frame)
if hasattr(student_config, 'preprocess_state'
) and student_config.preprocess_state is not None:
env = PreproWrapper(env,
prepro=greyscale,
shape=(80, 80, 1),
overwrite_render=student_config.overwrite_render)
# exploration strategy
if student_config.exp_policy == 'egreedy':
exp_schedule = LinearExploration(env, student_config.eps_begin,
student_config.eps_end,
student_config.eps_nsteps)
else:
exp_schedule = LinearGreedyExploration(env, student_config.eps_begin,
student_config.eps_end,
student_config.eps_nsteps)
# learning rate schedule
lr_schedule = LinearSchedule(student_config.lr_begin,
student_config.lr_end,
student_config.lr_nsteps)
# train model
model = DistilledQN(env, student_config)
model.run(exp_schedule, lr_schedule)
