def __init__(self):
CopterEnv.__init__(self)
# Initialize state
self._init()
# Action space = motors, rescaled from [0,1] to [-1,+1]
self.action_space = spaces.Box(np.array([-1] * 4), np.array([1] * 4))
# Observation space = full state space
self.observation_space = spaces.Box(np.array([-np.inf] * 12),
np.array([+np.inf] * 12))
def __init__(self, dt=.001, target=10, tolerance=1.0):
CopterEnv.__init__(self, dt)
self.target = target
self.tolerance = tolerance
# Action space = motors, rescaled from [0,1] to [-1,+1]
self.action_space = spaces.Box(np.array([-1]), np.array([1]))
# Observation space = altitude, vertical_velocity
self.observation_space = spaces.Box(np.array([0,-np.inf]), np.array([np.inf,np.inf]))
self._init()
def __init__(self):
CopterEnv.__init__(self, statedims=15)
# Initialize state
self._init()
# Action space = motors, rescaled from [0,1] to [-1,+1]
self.action_space = spaces.Box(np.array([-1]*4), np.array([1]*4))
# Observation space = full state space plus target position
self.observation_space = spaces.Box(np.array([-np.inf]*15), np.array([+np.inf]*15))
self.target_theta = 0
def step(self, action):
# Rescale action from [-1,+1] to [0,1]
motor = (1 + action[0]) / 2
# Use it for all four motors
motors = motor * np.ones(4)
# Call parent-class step() to do basic update
CopterEnv._update(self, motors)
# Dynamics uses NED coordinates, so negate to get altitude and vertical velocity
altitude = -self.state[4]
velocity = -self.state[5]
# Get a reward for every timestep on target
self.reward += (int)(abs(altitude - self.target) < self.tolerance)
# False = max_episodes in registry determines whether we're done
return (altitude, velocity), self.reward, False, {}
def step(self, action):
# Rescale action from [-1,+1] to [0,1]
motors = (1 + action) / 2
# Call parent-class method to do basic state update, return whether vehicle crashed
crashed = CopterEnv._update(self, motors)
# Integrate position
self.position += self.state[0:5:2]
# Reward is logarithm of Euclidean distance from origin
reward = np.sqrt(np.sum(self.position[0:2]**2))
return self.state, reward, crashed, {}
def step(self, action):
# Rescale action from [-1,+1] to [0,1]
motors = (1 + action) / 2
# Call parent-class method to do basic state update, return whether vehicle crashed
crashed = CopterEnv._update(self, motors)
# Update target position. For now we just make it go in circles
self.state[12] = 10 * np.cos(self.target_theta)
self.state[13] = 10 * np.sin(self.target_theta)
self.target_theta += .0025
# Fake up reward for now
reward = 0
return self.state, reward, crashed, {}
def step(self, action):
# Rescale action from [-1,+1] to [0,1]
motor = (1 + action[0]) / 2
# Use it for all four motors
motors = motor * np.ones(4)
# Call parent-class step() to do basic update
crashed = CopterEnv._update(self, motors)
# Dynamics uses NED coordinates, so negate to get altitude and vertical velocity
altitude = -self.state[4]
velocity = -self.state[5]
#costs = (altitude-self.target)**2 + .1*velocity**2 + .001*(motor**2)
costs = (altitude - self.target)**2 + velocity**2
# False = max_episodes in registry determines whether we're done
return (altitude, velocity), -costs, crashed, {}
def reset(self):
CopterEnv.reset(self)
self._init()
return -self.state[4:6]