mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
a7206ed702
* Multiple improvements and bug fixes:
* Using lazy stacking to save on memory when using a replay buffer
* Remove step counting for evaluation episodes
* Reset game between heatup and training
* Major bug fixes in NEC (is reproducing the paper results for pong now)
* Image input rescaling to 0-1 is now optional
* Change the terminal title to be the experiment name
* Observation cropping for atari is now optional
* Added random number of noop actions for gym to match the dqn paper
* Fixed a bug where the evaluation episodes won't start with the max possible ale lives
* Added a script for plotting the results of an experiment over all the atari games
78 lines
3.4 KiB
Python
78 lines
3.4 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 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], "max_action_value": np.max(actions_q_values)}
|
|
return action, action_value
|