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coach/agents/human_agent.py
2018-04-24 13:33:10 +02:00

74 lines
2.5 KiB
Python

#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import collections
import os
import pygame
from pandas.io import pickle
from agents import agent
import logger
import utils
class HumanAgent(agent.Agent):
def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
agent.Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
self.clock = pygame.time.Clock()
self.max_fps = int(self.tp.visualization.max_fps_for_human_control)
logger.screen.log_title("Human Control Mode")
available_keys = self.env.get_available_keys()
if available_keys:
logger.screen.log("Use keyboard keys to move. Press escape to quit. Available keys:")
logger.screen.log("")
for action, key in self.env.get_available_keys():
logger.screen.log("\t- {}: {}".format(action, key))
logger.screen.separator()
def train(self):
return 0
def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
action = self.env.get_action_from_user()
# keep constant fps
self.clock.tick(self.max_fps)
if not self.env.renderer.is_open:
self.save_replay_buffer_and_exit()
return action, {"action_value": 0}
def save_replay_buffer_and_exit(self):
replay_buffer_path = os.path.join(logger.logger.experiments_path, 'replay_buffer.p')
self.memory.tp = None
pickle.to_pickle(self.memory, replay_buffer_path)
logger.screen.log_title("Replay buffer was stored in {}".format(replay_buffer_path))
exit()
def log_to_screen(self, phase):
# log to logger.screen
logger.screen.log_dict(
collections.OrderedDict([
("Episode", self.current_episode),
("total reward", self.total_reward_in_current_episode),
("steps", self.total_steps_counter)
]),
prefix="Recording"
)