def build_low_level_ghavamzadeh(alg, params, mdp):
# FeaturesL
high = [150, 150, np.pi]
low = [0, 0, -np.pi]
n_tiles = [5, 5, 10]
low = np.array(low, dtype=np.float)
high = np.array(high, dtype=np.float)
n_tilings = 3
tilingsL = Tiles.generate(n_tilings=n_tilings,
n_tiles=n_tiles,
low=low,
high=high)
featuresL = Features(tilings=tilingsL)
mdp_info_agentL = MDPInfo(observation_space=spaces.Box(
low=np.array([0, 0]), high=np.array([150, 150]), shape=(2, )),
action_space=mdp.info.action_space,
gamma=0.99,
horizon=10000)
input_shape = (featuresL.size, )
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std = np.array([3e-2])
pi = DiagonalGaussianPolicy(mu=approximator, std=std)
agent = alg(pi, mdp_info_agentL, features=featuresL, **params)
return agent
def __init__(self, name, horizon, gamma):
self.__name__ = name
# MPD creation
self.env = gym.make(self.__name__)
self.env._max_episode_steps = np.inf # Hack to ignore gym time limit.
# MDP properties
assert not isinstance(self.env.observation_space,
gym_spaces.MultiDiscrete)
assert not isinstance(self.env.action_space, gym_spaces.MultiDiscrete)
action_space = self._convert_gym_space(self.env.action_space)
observation_space = self._convert_gym_space(self.env.observation_space)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
if isinstance(action_space, Discrete):
self._convert_action = self._convert_action_function
else:
self._convert_action = self._no_convert
if isinstance(observation_space,
Discrete) and len(observation_space.size) > 1:
self._convert_state = self._convert_state_function
else:
self._convert_state = self._no_convert
super(Gym, self).__init__(mdp_info)
def __init__(self, **kwargs):
# Define environment properties
high_x = np.array([5.0, 5.0, np.pi])
low_x = -high_x
high_u = np.array([1.0, 3.0])
low_u = -high_u
observation_space = spaces.Box(low=low_x, high=high_x)
action_space = spaces.Box(low=low_u, high=high_u)
gamma = 0.9
horizon = 400
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
hz = 10.0
super(TurtlebotGazebo, self).__init__('turtlebot_gazebo', mdp_info, hz, **kwargs)
# subscribe to /cmd_vel topic to publish the setpoint
self._pub = rospy.Publisher('/cmd_vel', Twist, queue_size=1)
# subscribe to /gazebo/model_states to get the position of the turtlebot
model_state_service_name = '/gazebo/get_model_state'
rospy.wait_for_service(model_state_service_name)
self._model_state_service = rospy.ServiceProxy(model_state_service_name, GetModelState)
def build_discretized_agent(alg, params, n, optim, loss, mdp, eps, n_features,
use_cuda):
high = mdp.info.observation_space.high
low = mdp.info.observation_space.low
observation_space = spaces.Box(low=low, high=high)
action_space = spaces.Discrete(n)
mdp_info = MDPInfo(observation_space=observation_space,
action_space=action_space,
gamma=mdp.info.gamma,
horizon=mdp.info.horizon)
pi = Boltzmann(eps)
approximator_params = dict(network=Network,
optimizer=optim,
loss=loss,
n_features=n_features,
input_shape=mdp_info.observation_space.shape,
output_shape=mdp_info.action_space.size,
n_actions=mdp_info.action_space.n,
use_cuda=use_cuda)
agent = alg(PyTorchApproximator,
pi,
mdp_info,
approximator_params=approximator_params,
**params)
return agent
def __init__(self, n_steps_action=3, viz_speed=100, small=False):
self.__name__ = 'ShipSteeringMultiGate'
self.n_steps_action = n_steps_action
self.viz_speed = viz_speed
# MDP parameters
self.no_of_gates = 4
self.small = small
self.field_size = 500 if small else 1000
low = np.array([0, 0, -np.pi, -np.pi / 12., 0])
high = np.array([
self.field_size, self.field_size, np.pi, np.pi / 12.,
self.no_of_gates
])
self.omega_max = np.array([np.pi / 12.])
self._v = 3.
self._T = 5.
self._dt = .2
gate_1s = np.array([75, 175]) if small else np.array([150, 350])
gate_1e = np.array([125, 175]) if small else np.array([250, 350])
gate_1 = np.array([gate_1s, gate_1e])
gate_2s = np.array([150, 300]) if small else np.array([300, 600])
gate_2e = np.array([200, 300]) if small else np.array([400, 600])
gate_2 = np.array([gate_2s, gate_2e])
gate_3s = np.array([250, 350]) if small else np.array([500, 700])
gate_3e = np.array([300, 350]) if small else np.array([600, 700])
gate_3 = np.array([gate_3s, gate_3e])
gate_4s = np.array([150, 425]) if small else np.array([300, 850])
gate_4e = np.array([200, 425]) if small else np.array([400, 850])
gate_4 = np.array([gate_4s, gate_4e])
self._gate_list = gate_1, gate_2, gate_3, gate_4
# MDP properties
observation_space = spaces.Box(low=low, high=high)
action_space = spaces.Box(low=-self.omega_max, high=self.omega_max)
horizon = 5000
gamma = .99
self._out_reward = -10000
self.correct_order = False
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
self._viewer = Viewer(self.field_size,
self.field_size,
background=(66, 131, 237))
super(ShipSteeringMultiGate, self).__init__(mdp_info)
def __init__(self, domain_name, task_name, horizon, gamma, task_kwargs=None,
dt=.01, width_screen=480, height_screen=480, camera_id=0):
"""
Constructor.
Args:
domain_name (str): name of the environment;
task_name (str): name of the task of the environment;
horizon (int): the horizon;
gamma (float): the discount factor;
task_kwargs (dict, None): parameters of the task;
dt (float, .01): duration of a control step;
width_screen (int, 480): width of the screen;
height_screen (int, 480): height of the screen;
camera_id (int, 0): position of camera to render the environment;
"""
# MDP creation
if task_kwargs is None:
task_kwargs = dict()
task_kwargs['time_limit'] = np.inf # Hack to ignore dm_control time limit.
self.env = suite.load(domain_name, task_name, task_kwargs=task_kwargs)
# MDP properties
action_space = self._convert_action_space(self.env.action_spec())
observation_space = self._convert_observation_space(self.env.observation_spec())
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
self._viewer = ImageViewer((width_screen, height_screen), dt)
self._camera_id = camera_id
super().__init__(mdp_info)
def __init__(self, name, width=84, height=84, ends_at_life=False):
"""
Constructor.
Args:
name (str): id name of the Atari game in Gym;
width (int, 84): width of the screen;
height (int, 84): height of the screen;
ends_at_life (bool, False): whether the episode ends when a life is
lost or not.
"""
self.__name__ = name
# MPD creation
self.env = gym.make(self.__name__)
# MDP parameters
self.img_size = (width, height)
self._episode_ends_at_life = ends_at_life
self._max_lives = self.env.env.ale.lives()
self._lives = self._max_lives
# MDP properties
action_space = Discrete(self.env.action_space.n)
observation_space = Box(low=0.,
high=255.,
shape=(self.img_size[1], self.img_size[0]))
horizon = np.inf
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(Atari, self).__init__(mdp_info)
def __init__(self, A, B, Q, R, random_init=False, gamma=0.9, horizon=50):
"""
Constructor.
Args:
A (np.ndarray): the state dynamics matrix;
B (np.ndarray): the action dynamics matrix;
Q (np.ndarray): reward weight matrix for state;
R (np.ndarray): reward weight matrix for action;
random_init (bool, False): start from a random state;
gamma (float, 0.9): discount factor;
horizon (int, 50): horizon of the mdp.
"""
self.A = A
self.B = B
self.Q = Q
self.R = R
self.random_init = random_init
# MDP properties
high_x = np.inf * np.ones(A.shape[0])
low_x = -high_x
high_u = np.inf * np.ones(B.shape[0])
low_u = -high_u
observation_space = spaces.Box(low=low_x, high=high_x)
action_space = spaces.Box(low=low_u, high=high_u)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(LQR, self).__init__(mdp_info)
def __init__(self, small=True):
self.__name__ = 'ShipSteering'
# MDP parameters
self.field_size = 150 if small else 1000
low = np.array([0, 0, -np.pi, -np.pi / 12.])
high = np.array([self.field_size, self.field_size, np.pi, np.pi / 12.])
self.omega_max = np.array([np.pi / 12.])
self._v = 3.
self._T = 5.
self._dt = .2
self._gate_s = np.empty(2)
self._gate_e = np.empty(2)
self._gate_s[0] = 100 if small else 900
self._gate_s[1] = 120 if small else 920
self._gate_e[0] = 120 if small else 920
self._gate_e[1] = 100 if small else 900
# MDP properties
observation_space = spaces.Box(low=low, high=high)
action_space = spaces.Box(low=-self.omega_max, high=self.omega_max)
horizon = 5000
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(ShipSteering, self).__init__(mdp_info)
def __init__(self, grid_map_file, height_window=84, width_window=84):
self.__name__ = 'GridWorldPixelGenerator'
self.window_size = (width_window, height_window)
self._symbols = {
'.': 0.,
'S': 63.75,
'*': 127.5,
'#': 191.25,
'G': 255.
}
self._grid, start, goal = self._generate(grid_map_file)
self._initial_grid = deepcopy(self._grid)
height = self._grid.shape[0]
width = self._grid.shape[1]
assert height_window % height == 0 and width_window % width == 0
# MDP properties
observation_space = spaces.Box(low=0.,
high=255.,
shape=(self.window_size[1],
self.window_size[0]))
action_space = spaces.Discrete(5)
horizon = 100
gamma = .9
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(GridWorldPixelGenerator, self).__init__(mdp_info, height, width,
start, goal)
def __init__(self, small=True, hard=False):
"""
Constructor.
Args:
small (bool, True): whether to use a small state space or not.
hard (bool, False): whether to use -100 as reward for going
outside or -10000. With -100 reward the
environment is considerably harder.
"""
self.__name__ = 'ShipSteeringStraight'
# MDP parameters
self.field_size = 150
low = np.array([0, 0, -np.pi, -np.pi / 12.])
high = np.array([self.field_size, self.field_size, np.pi, np.pi / 12.])
self.omega_max = np.array([np.pi / 12.])
self._v = 3.
self._T = 5.
self._dt = .2
self.goal_pos = np.array([140, 75])
self._out_reward = -100
self._success_reward = 100
# MDP properties
observation_space = spaces.Box(low=low, high=high)
action_space = spaces.Box(low=-self.omega_max, high=self.omega_max)
horizon = 5000
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(ShipSteeringStraight, self).__init__(mdp_info)
def __init__(self, height, width, goal, start=(0, 0)):
# MDP properties
observation_space = spaces.Discrete(height * width)
action_space = spaces.Discrete(4)
horizon = 100
gamma = .9
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super().__init__(mdp_info, height, width, start, goal)
def __init__(self,
start=None,
goal=None,
goal_threshold=.1,
noise_step=.025,
noise_reward=0,
reward_goal=0.,
thrust=.05,
puddle_center=None,
puddle_width=None,
gamma=.99,
horizon=5000):
"""
Constructor.
Args:
start (np.array, None): starting position of the agent;
goal (np.array, None): goal position;
goal_threshold (float, .1): distance threshold of the agent from the
goal to consider it reached;
noise_step (float, .025): noise in actions;
noise_reward (float, 0): standard deviation of gaussian noise in reward;
reward_goal (float, 0): reward obtained reaching goal state;
thrust (float, .05): distance walked during each action;
puddle_center (np.array, None): center of the puddle;
puddle_width (np.array, None): width of the puddle;
"""
# MDP parameters
self._start = np.array([.2, .4]) if start is None else start
self._goal = np.array([1., 1.]) if goal is None else goal
self._goal_threshold = goal_threshold
self._noise_step = noise_step
self._noise_reward = noise_reward
self._reward_goal = reward_goal
self._thrust = thrust
puddle_center = [[.3, .6], [.4, .5], [.8, .9]
] if puddle_center is None else puddle_center
self._puddle_center = [np.array(center) for center in puddle_center]
puddle_width = [[.1, .03], [.03, .1], [.03, .1]
] if puddle_width is None else puddle_width
self._puddle_width = [np.array(width) for width in puddle_width]
self._actions = [np.zeros(2) for _ in range(5)]
for i in range(4):
self._actions[i][i // 2] = thrust * (i % 2 * 2 - 1)
# MDP properties
action_space = Discrete(5)
observation_space = Box(0., 1., shape=(2, ))
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
self._pixels = None
self._viewer = Viewer(1.0, 1.0)
super().__init__(mdp_info)
def __init__(self, height=3, width=3, goal=(0, 2), start=(2, 0)):
# MDP properties
observation_space = spaces.Discrete(height * width)
action_space = spaces.Discrete(4)
horizon = np.inf
gamma = .95
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(GridWorldVanHasselt, self).__init__(mdp_info, height, width,
start, goal)
def build_high_level_agent(alg, params, mdp, epsilon):
pi = EpsGreedy(epsilon=epsilon, )
mdp_info_high = MDPInfo(observation_space=spaces.Discrete(16),
action_space=spaces.Discrete(4),
gamma=mdp.info.gamma,
horizon=100)
agent = alg(pi, mdp_info_high, **params)
return agent
def __init__(self,
name,
width=84,
height=84,
ends_at_life=False,
max_pooling=True,
history_length=4,
max_no_op_actions=30):
"""
Constructor.
Args:
name (str): id name of the Atari game in Gym;
width (int, 84): width of the screen;
height (int, 84): height of the screen;
ends_at_life (bool, False): whether the episode ends when a life is
lost or not;
max_pooling (bool, True): whether to do max-pooling or
average-pooling of the last two frames when using NoFrameskip;
history_length (int, 4): number of frames to form a state;
max_no_op_actions (int, 30): maximum number of no-op action to
execute at the beginning of an episode.
"""
# MPD creation
if 'NoFrameskip' in name:
self.env = MaxAndSkip(gym.make(name), history_length, max_pooling)
else:
self.env = gym.make(name)
# MDP parameters
self.img_size = (width, height)
self._episode_ends_at_life = ends_at_life
self._max_lives = self.env.unwrapped.ale.lives()
self._lives = self._max_lives
self._force_fire = None
self._real_reset = True
self._max_no_op_actions = max_no_op_actions
self._history_length = history_length
self._current_no_op = None
assert self.env.unwrapped.get_action_meanings()[0] == 'NOOP'
# MDP properties
action_space = Discrete(self.env.action_space.n)
observation_space = Box(low=0.,
high=255.,
shape=(self.img_size[1], self.img_size[0]))
horizon = np.inf # the gym time limit is used.
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super().__init__(mdp_info)
def build_high_level_agent(alg, params, mdp):
epsilon = Parameter(value=0.1)
pi = EpsGreedy(epsilon=epsilon)
gamma = 1.0
mdp_info_agentH = MDPInfo(observation_space=spaces.Discrete(400),
action_space=spaces.Discrete(8),
gamma=gamma,
horizon=10000)
agent = alg(policy=pi, mdp_info=mdp_info_agentH, **params)
return agent
def __init__(self, grid_map):
self.__name__ = 'GridWorldGenerator'
self._grid, height, width, start, goal = self._generate(grid_map)
# MDP properties
observation_space = spaces.Discrete(height * width)
action_space = spaces.Discrete(4)
horizon = 100
gamma = .9
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(GridWorldGenerator, self).__init__(mdp_info, height, width,
start, goal)
def __init__(self,
A,
B,
Q,
R,
max_pos=np.inf,
max_action=np.inf,
random_init=False,
episodic=False,
gamma=0.9,
horizon=50):
"""
Constructor.
Args:
A (np.ndarray): the state dynamics matrix;
B (np.ndarray): the action dynamics matrix;
Q (np.ndarray): reward weight matrix for state;
R (np.ndarray): reward weight matrix for action;
max_pos (float, np.inf): maximum value of the state;
max_action (float, np.inf): maximum value of the action;
random_init (bool, False): start from a random state;
episodic (bool, False): end the episode when the state goes over
the threshold;
gamma (float, 0.9): discount factor;
horizon (int, 50): horizon of the mdp.
"""
self.A = A
self.B = B
self.Q = Q
self.R = R
self._max_pos = max_pos
self._max_action = max_action
self._episodic = episodic
self.random_init = random_init
# MDP properties
high_x = self._max_pos * np.ones(A.shape[0])
low_x = -high_x
high_u = self._max_action * np.ones(B.shape[0])
low_u = -high_u
observation_space = spaces.Box(low=low_x, high=high_x)
action_space = spaces.Box(low=low_u, high=high_u)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super().__init__(mdp_info)
def __init__(self,
m=2.,
M=8.,
l=.5,
g=9.8,
mu=1e-2,
max_u=50.,
noise_u=10.,
horizon=3000,
gamma=.95):
"""
Constructor.
Args:
m (float, 2.0): mass of the pendulum;
M (float, 8.0): mass of the cart;
l (float, .5): length of the pendulum;
g (float, 9.8): gravity acceleration constant;
mu (float, 1e-2): friction constant of the pendulum;
max_u (float, 50.): maximum allowed input torque;
noise_u (float, 10.): maximum noise on the action;
horizon (int, 3000): horizon of the problem;
gamma (int, .95): discount factor.
"""
# MDP parameters
self._m = m
self._M = M
self._l = l
self._g = g
self._alpha = 1 / (self._m + self._M)
self._mu = mu
self._dt = .1
self._max_u = max_u
self._noise_u = noise_u
high = np.array([np.inf, np.inf])
# MDP properties
observation_space = spaces.Box(low=-high, high=high)
action_space = spaces.Discrete(3)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
self._viewer = Viewer(2.5 * l, 2.5 * l)
self._last_u = None
self._state = None
super().__init__(mdp_info)
def build_high_level_agent(alg, params, mdp, mu, sigma):
features = Features(basis_list=[PolynomialBasis()])
approximator = Regressor(LinearApproximator,
input_shape=(features.size, ),
output_shape=(2, ))
approximator.set_weights(mu)
pi1 = DiagonalGaussianPolicy(mu=approximator, std=sigma)
lim = mdp.info.observation_space.high[0]
mdp_info_agent = MDPInfo(observation_space=mdp.info.observation_space,
action_space=spaces.Box(0, lim, (2, )),
gamma=1.0,
horizon=100)
agent = alg(pi1, mdp_info_agent, features=features, **params)
return agent
def __init__(self,
random_start=False,
m=1.,
l=1.,
g=9.8,
mu=1e-2,
max_u=5.,
horizon=5000,
gamma=.99):
"""
Constructor.
Args:
random_start (bool, False): whether to start from a random position
or from the horizontal one;
m (float, 1.0): mass of the pendulum;
l (float, 1.0): length of the pendulum;
g (float, 9.8): gravity acceleration constant;
mu (float, 1e-2): friction constant of the pendulum;
max_u (float, 5.0): maximum allowed input torque;
horizon (int, 5000): horizon of the problem;
gamma (int, .99): discount factor.
"""
# MDP parameters
self._m = m
self._l = l
self._g = g
self._mu = mu
self._random = random_start
self._dt = .01
self._max_u = max_u
self._max_omega = 5 / 2 * np.pi
high = np.array([np.pi, self._max_omega])
# MDP properties
observation_space = spaces.Box(low=-high, high=high)
action_space = spaces.Box(low=np.array([-max_u]),
high=np.array([max_u]))
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
self._viewer = Viewer(2.5 * l, 2.5 * l)
self._last_u = None
super().__init__(mdp_info)
def build_low_level_agent(alg, params, mdp):
features = Features(
basis_list=[PolynomialBasis(dimensions=[0], degrees=[1])])
pi = DeterministicControlPolicy(weights=np.array([0]))
mu = np.zeros(pi.weights_size)
sigma = 1e-3 * np.ones(pi.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
mdp_info_agent2 = MDPInfo(observation_space=spaces.Box(
-np.pi, np.pi, (1, )),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma,
horizon=100)
agent = alg(distribution, pi, mdp_info_agent2, features=features, **params)
return agent
def build_high_level_agent(alg, params, mdp, mu, std):
tilings = Tiles.generate(n_tilings=1,
n_tiles=[10, 10],
low=mdp.info.observation_space.low[:2],
high=mdp.info.observation_space.high[:2])
features = Features(tilings=tilings)
input_shape = (features.size, )
mu_approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=(1, ))
std_approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=(1, ))
w_mu = mu * np.ones(mu_approximator.weights_size)
mu_approximator.set_weights(w_mu)
w_std = std * np.ones(std_approximator.weights_size)
mu_approximator.set_weights(w_std)
pi = StateLogStdGaussianPolicy(mu=mu_approximator,
log_std=std_approximator)
obs_low = np.array(
[mdp.info.observation_space.low[0], mdp.info.observation_space.low[1]])
obs_high = np.array([
mdp.info.observation_space.high[0], mdp.info.observation_space.high[1]
])
mdp_info_agent1 = MDPInfo(observation_space=spaces.Box(obs_low,
obs_high,
shape=(2, )),
action_space=spaces.Box(
mdp.info.observation_space.low[2],
mdp.info.observation_space.high[2],
shape=(1, )),
gamma=1,
horizon=10)
agent = alg(policy=pi,
mdp_info=mdp_info_agent1,
features=features,
**params)
return agent
def __init__(self, random_start=False, goal_distance=1.0):
"""
Constructor.
Args:
random_start: whether to start from a random position or from the
horizontal one
"""
# MDP parameters
gamma = 0.99
self.Mr = 0.3 * 2
self.Mp = 2.55
self.Ip = 2.6e-2
self.Ir = 4.54e-4 * 2
self.l = 13.8e-2
self.r = 5.5e-2
self.dt = 1e-2
self.g = 9.81
self.max_u = 5
self._random = random_start
self._goal_distance = goal_distance
high = np.array([2 * self._goal_distance, np.pi, 15, 75])
# MDP properties
observation_space = spaces.Box(low=-high, high=high)
action_space = spaces.Box(low=np.array([-self.max_u]),
high=np.array([self.max_u]))
horizon = 1500
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
env_width = 4 * goal_distance
env_height = 2.5 * 2 * self.l
width = 800
height = int(width * env_height / env_width)
self._viewer = Viewer(env_width, env_height, width, height)
super(SegwayLinearMotion, self).__init__(mdp_info)
def __init__(self,
name,
width=84,
height=84,
ends_at_life=False,
max_pooling=True):
"""
Constructor.
Args:
name (str): id name of the Atari game in Gym;
width (int, 84): width of the screen;
height (int, 84): height of the screen;
ends_at_life (bool, False): whether the episode ends when a life is
lost or not;
max_pooling (bool, True): whether to do max-pooling or
average-pooling of the last two frames when using NoFrameskip.
"""
# MPD creation
if 'NoFrameskip' in name:
skip = 3 if 'SpaceInvaders' in name else 4
self.env = MaxAndSkip(gym.make(name), skip, max_pooling)
else:
self.env = gym.make(name)
# MDP parameters
self.img_size = (width, height)
self._episode_ends_at_life = ends_at_life
self._max_lives = self.env.unwrapped.ale.lives()
self._lives = self._max_lives
self._force_fire = None
self._real_reset = True
# MDP properties
action_space = Discrete(self.env.action_space.n)
observation_space = Box(low=0.,
high=255.,
shape=(self.img_size[1], self.img_size[0]))
horizon = np.inf
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(Atari, self).__init__(mdp_info)
def __init__(self, small=True, n_steps_action=3):
"""
Constructor.
Args:
small (bool, True): whether to use a small state space or not.
n_steps_action (int, 3): number of integration intervals for each
step of the mdp.
"""
# MDP parameters
self.field_size = 150 if small else 1000
low = np.array([0, 0, -np.pi, -np.pi / 12.])
high = np.array([self.field_size, self.field_size, np.pi, np.pi / 12.])
self.omega_max = np.array([np.pi / 12.])
self._v = 3.
self._T = 5.
self._dt = .2
self._gate_s = np.empty(2)
self._gate_e = np.empty(2)
self._gate_s[0] = 100 if small else 350
self._gate_s[1] = 120 if small else 400
self._gate_e[0] = 120 if small else 450
self._gate_e[1] = 100 if small else 400
self._out_reward = -100
self._success_reward = 0
self._small = small
self._state = None
self.n_steps_action = n_steps_action
# MDP properties
observation_space = spaces.Box(low=low, high=high)
action_space = spaces.Box(low=-self.omega_max, high=self.omega_max)
horizon = 5000
gamma = .99
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
# Visualization
self._viewer = Viewer(self.field_size,
self.field_size,
background=(66, 131, 237))
super(ShipSteering, self).__init__(mdp_info)
def __init__(self):
self.__name__ = 'CarOnHill'
# MDP parameters
self.max_pos = 1.
self.max_velocity = 3.
high = np.array([self.max_pos, self.max_velocity])
self._g = 9.81
self._m = 1
self._dt = .1
self._discrete_actions = [-4., 4.]
# MDP properties
observation_space = spaces.Box(low=-high, high=high)
action_space = spaces.Discrete(2)
horizon = 100
gamma = .95
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super(CarOnHill, self).__init__(mdp_info)
def __init__(self, horizon=100, gamma=.95):
"""
Constructor.
"""
# MDP parameters
self.max_pos = 1.
self.max_velocity = 3.
high = np.array([self.max_pos, self.max_velocity])
self._g = 9.81
self._m = 1.
self._dt = .1
self._discrete_actions = [-4., 4.]
# MDP properties
observation_space = spaces.Box(low=-high, high=high)
action_space = spaces.Discrete(2)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
super().__init__(mdp_info)
def build_agent_low(alg, params, std, mdp):
approximator = Regressor(LinearApproximator,
input_shape=(3, ),
output_shape=(1, ))
n_weights = approximator.weights_size
mu = np.zeros(n_weights)
sigma = std * np.ones(n_weights)
pi = DeterministicControlPolicy(approximator)
dist = GaussianDiagonalDistribution(mu, sigma)
# Agent Low
mdp_info = MDPInfo(
observation_space=spaces.Box(
low=mdp.info.observation_space.low[1:], # FIXME FALSE
high=mdp.info.observation_space.high[1:], # FIXME FALSE
),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma,
horizon=mdp.info.horizon)
return alg(dist, pi, mdp_info, **params)