mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
95 lines
4.1 KiB
Python
95 lines
4.1 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 numpy as np
|
|
|
|
from agents.agent import Agent
|
|
from architectures.network_wrapper import NetworkWrapper
|
|
from utils import RunPhase, Signal
|
|
|
|
|
|
class ValueOptimizationAgent(Agent):
|
|
def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=True):
|
|
Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
|
|
self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
|
|
self.replicated_device, self.worker_device)
|
|
self.networks.append(self.main_network)
|
|
self.q_values = Signal("Q")
|
|
self.signals.append(self.q_values)
|
|
|
|
self.reset_game(do_not_reset_env=True)
|
|
|
|
# Algorithms for which q_values are calculated from predictions will override this function
|
|
def get_q_values(self, prediction):
|
|
return prediction
|
|
|
|
def tf_input_state(self, curr_state):
|
|
"""
|
|
convert curr_state into input tensors tensorflow is expecting.
|
|
|
|
TODO: move this function into Agent and use in as many agent implementations as possible
|
|
currently, other agents will likely not work with environment measurements.
|
|
This will become even more important as we support more complex and varied environment states.
|
|
"""
|
|
# convert to batch so we can run it through the network
|
|
observation = np.expand_dims(np.array(curr_state['observation']), 0)
|
|
if self.tp.agent.use_measurements:
|
|
measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
|
|
tf_input_state = [observation, measurements]
|
|
else:
|
|
tf_input_state = observation
|
|
return tf_input_state
|
|
|
|
def get_prediction(self, curr_state):
|
|
return self.main_network.online_network.predict(self.tf_input_state(curr_state))
|
|
|
|
def _validate_action(self, policy, action):
|
|
if np.array(action).shape != ():
|
|
raise ValueError((
|
|
'The exploration_policy {} returned a vector of actions '
|
|
'instead of a single action. ValueOptimizationAgents '
|
|
'require exploration policies which return a single action.'
|
|
).format(policy.__class__.__name__))
|
|
|
|
def choose_action(self, curr_state, phase=RunPhase.TRAIN):
|
|
prediction = self.get_prediction(curr_state)
|
|
actions_q_values = self.get_q_values(prediction)
|
|
|
|
# choose action according to the exploration policy and the current phase (evaluating or training the agent)
|
|
if phase == RunPhase.TRAIN:
|
|
exploration_policy = self.exploration_policy
|
|
else:
|
|
exploration_policy = self.evaluation_exploration_policy
|
|
|
|
action = exploration_policy.get_action(actions_q_values)
|
|
self._validate_action(exploration_policy, action)
|
|
|
|
# this is for bootstrapped dqn
|
|
if type(actions_q_values) == list and len(actions_q_values) > 0:
|
|
actions_q_values = actions_q_values[self.exploration_policy.selected_head]
|
|
actions_q_values = actions_q_values.squeeze()
|
|
|
|
# store the q values statistics for logging
|
|
self.q_values.add_sample(actions_q_values)
|
|
|
|
# store information for plotting interactively (actual plotting is done in agent)
|
|
if self.tp.visualization.plot_action_values_online:
|
|
for idx, action_name in enumerate(self.env.actions_description):
|
|
self.episode_running_info[action_name].append(actions_q_values[idx])
|
|
|
|
action_value = {"action_value": actions_q_values[action]}
|
|
return action, action_value
|