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Release 0.9

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Main changes are detailed below:

New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)

API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym

Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility
This commit is contained in:
Itai Caspi
2017-12-19 19:27:16 +02:00
committed by GitHub
parent 11faf19649
commit 125c7ee38d
41 changed files with 1713 additions and 260 deletions
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94
environments/gym_environment_wrapper.py
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@@ -15,8 +15,10 @@
#
import sys
from logger import *
import gym
import numpy as np
import time
try:
import roboschool
from OpenGL import GL
@@ -40,8 +42,6 @@ from gym import wrappers
from utils import force_list, RunPhase
from environments.environment_wrapper import EnvironmentWrapper
i = 0
class GymEnvironmentWrapper(EnvironmentWrapper):
def __init__(self, tuning_parameters):
@@ -53,29 +53,30 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
self.env.seed(self.seed)
# self.env_spec = gym.spec(self.env_id)
self.env.frameskip = self.frame_skip
self.discrete_controls = type(self.env.action_space) != gym.spaces.box.Box
# pybullet requires rendering before resetting the environment, but other gym environments (Pendulum) will crash
try:
if self.is_rendered:
self.render()
except:
pass
o = self.reset(True)['observation']
self.observation = self.reset(True)['observation']
# render
if self.is_rendered:
self.render()
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
self.is_state_type_image = len(o.shape) > 1
self.is_state_type_image = len(self.observation.shape) > 1
if self.is_state_type_image:
self.width = o.shape[1]
self.height = o.shape[0]
self.width = self.observation.shape[1]
self.height = self.observation.shape[0]
else:
self.width = o.shape[0]
self.width = self.observation.shape[0]
# action space
self.actions_description = {}
if hasattr(self.env.unwrapped, 'get_action_meanings'):
self.actions_description = self.env.unwrapped.get_action_meanings()
if self.discrete_controls:
self.action_space_size = self.env.action_space.n
self.action_space_abs_range = 0
@@ -85,34 +86,31 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
self.action_space_low = self.env.action_space.low
self.action_space_abs_range = np.maximum(np.abs(self.action_space_low), np.abs(self.action_space_high))
self.actions = {i: i for i in range(self.action_space_size)}
self.key_to_action = {}
if hasattr(self.env.unwrapped, 'get_keys_to_action'):
self.key_to_action = self.env.unwrapped.get_keys_to_action()
# measurements
self.timestep_limit = self.env.spec.timestep_limit
self.current_ale_lives = 0
self.measurements_size = len(self.step(0)['info'].keys())
# env intialization
self.observation = o
self.reward = 0
self.done = False
self.last_action = self.actions[0]
def render(self):
self.env.render()
def step(self, action_idx):
def _update_state(self):
if hasattr(self.env.env, 'ale'):
if self.phase == RunPhase.TRAIN and hasattr(self, 'current_ale_lives'):
# signal termination for life loss
if self.current_ale_lives != self.env.env.ale.lives():
self.done = True
self.current_ale_lives = self.env.env.ale.lives()
def _take_action(self, action_idx):
if action_idx is None:
action_idx = self.last_action_idx
self.last_action_idx = action_idx
if self.discrete_controls:
action = self.actions[action_idx]
else:
action = action_idx
if hasattr(self.env.env, 'ale'):
prev_ale_lives = self.env.env.ale.lives()
# pendulum-v0 for example expects a list
if not self.discrete_controls:
# catching cases where the action for continuous control is a number instead of a list the
@@ -128,42 +126,26 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
self.observation, self.reward, self.done, self.info = self.env.step(action)
if hasattr(self.env.env, 'ale') and self.phase == RunPhase.TRAIN:
# signal termination for breakout life loss
if prev_ale_lives != self.env.env.ale.lives():
self.done = True
def _preprocess_observation(self, observation):
if any(env in self.env_id for env in ["Breakout", "Pong"]):
# crop image
self.observation = self.observation[34:195, :, :]
if self.is_rendered:
self.render()
return {'observation': self.observation,
'reward': self.reward,
'done': self.done,
'action': self.last_action_idx,
'info': self.info}
observation = observation[34:195, :, :]
return observation
def _restart_environment_episode(self, force_environment_reset=False):
# prevent reset of environment if there are ale lives left
if "Breakout" in self.env_id and self.env.env.ale.lives() > 0 and not force_environment_reset:
if (hasattr(self.env.env, 'ale') and self.env.env.ale.lives() > 0) \
and not force_environment_reset and not self.env._past_limit():
return self.observation
if self.seed:
self.env.seed(self.seed)
observation = self.env.reset()
while observation is None:
observation = self.step(0)['observation']
if "Breakout" in self.env_id:
# crop image
observation = observation[34:195, :, :]
self.observation = self.env.reset()
while self.observation is None:
self.step(0)
self.observation = observation
return observation
return self.observation
def get_rendered_image(self):
return self.env.render(mode='rgb_array')