class MultiAgent_DDPG(BaseEnvironment):
metadata = {
'render.modes': ['human', 'rgb_array', 'state_pixels'],
'video.frames_per_second': render2d.FPS
}
def __init__(self,
env_config,
test_mode=False,
render_mode='2d',
verbose=False):
"""
The __init__ method declares all class atributes and calls
the self.reset() to intialize them properly.
Parameters
----------
env_config : dict
Configuration parameters for the environment.
The default values are set in __init__.py
test_mode : bool
If test_mode is True, the environment will not be autonatically reset
due to too low cumulative reward or too large distance from the path.
render_mode : {'2d', '3d', 'both'}
Whether to use 2d or 3d rendering. 'both' is currently broken.
verbose
Whether to print debugging information.
"""
self.test_mode = test_mode
self.render_mode = render_mode
self.verbose = verbose
self.config = env_config
# Setting dimension of observation vector
self.n_observations = len(
Vessel.NAVIGATION_FEATURES) + 4 * self.config["n_sectors"]
self.episode = 0
self.total_t_steps = 0
self.t_step = 0
self.history = []
# Declaring attributes
#self.obstacles = None
self.main_vessel = None
#self.path = None
self.reached_goal = None
self.collision = None
self.progress = None
self.cumulative_reward = None
self.last_reward = None
self.last_episode = None
self.rng = None
self._tmp_storage = None
self._action_space = gym.spaces.Box(low=np.array([-1, -1]),
high=np.array([1, 1]),
dtype=np.float32)
self._observation_space = gym.spaces.Box(
low=np.array([-1] * self.n_observations),
high=np.array([1] * self.n_observations),
dtype=np.float32)
# Initializing rendering
self.viewer2d = None
self.viewer3d = None
if self.render_mode == '2d' or self.render_mode == 'both':
render2d.init_env_viewer(self)
if self.render_mode == '3d' or self.render_mode == 'both':
render3d.init_env_viewer(self,
autocamera=self.config["autocamera3d"])
self.reset()
@property
def path(self):
return self.main_vessel.path
@property
def obstacles(self):
return self.main_vessel.obstacles
@property
def vessel(self):
return self.main_vessel
def _generate(self):
print('In GENERATE in MA')
self.main_vessel = Vessel(self.config,
width=self.config["vessel_width"])
prog = 0
self.path_prog_hist = np.array([prog])
self.max_path_prog = prog
self.rewarder = MultiRewarder(self.main_vessel)
print(f'Ownship created!')
self.moving_obstacles = [self.main_vessel]
self.static_obstacles = []
self.queued_vessels = []
#Adding static obstacles
#for _ in range(8):
# obstacle = CircularObstacle(*helpers.generate_obstacle(self.rng, self.main_vessel.path, self.main_vessel))
# self.static_obstacles.append(obstacle)
self._vessel_count = 1
#Adding moving obstacles (ships)
curr_vessel_count = self._vessel_count
for i in range(curr_vessel_count, curr_vessel_count + 5):
#obst_speed = np.random.random()
ship = Vessel(self.config,
width=self.config["vessel_width"],
index=i,
vessel_pos=self.main_vessel.position)
self.moving_obstacles.append(ship)
print(f'Ship {i} has been created')
self._vessel_count += 1
for ship in self.moving_obstacles:
other_ships = [
x for x in self.moving_obstacles if x.index != ship.index
]
#ship.obstacles.extend(other_ships)
ship.obstacles = np.hstack([self.static_obstacles, other_ships])
print('Exiting GENERATE in MA')
def step(self, action: list) -> (np.ndarray, float, bool, dict):
"""
Steps the environment by one timestep. Returns observation, reward, done, info.
Parameters
----------
action : np.ndarray
[thrust_input, torque_input].
Returns
-------
obs : np.ndarray
Observation of the environment after action is performed.
reward : double
The reward for performing action at his timestep.
done : bool
If True the episode is ended, due to either a collision or having reached the goal position.
info : dict
Dictionary with data used for reporting or debugging
"""
print('IN STEP')
if len(self.queued_vessels) == 0:
current_vessel = self.main_vessel
else:
current_vessel = self.queued_vessels.pop(0)
current_index = current_vessel.index
print(f'Current vessel is ship {current_index}')
#[vessel.update_without_agent(self.config["t_step_size"]) for vessel in self.moving_obstacles if vessel.index != current_index]
action[0] = (action[0] + 1) / 2
current_vessel.step(action)
reward = self.rewarder.calculate(current_vessel)
self.cumulative_reward += reward
vessel_data = self.main_vessel.req_latest_data()
self.collision = vessel_data['collision']
self.reached_goal = vessel_data['reached_goal']
self.progress = vessel_data['progress']
info = {}
info['collision'] = self.collision
info['reached_goal'] = self.reached_goal
info['progress'] = self.progress
done = self._isdone()
self._save_latest_step()
self.moving_obstacles = self.main_vessel.nearby_vessels
#Adding moving obstacles (ships)
if not self.t_step % 150:
#print(f'Time step: {self.t_step}, position of vessel: {self.main_vessel.position}')
curr_vessel_count = self._vessel_count
for i in range(curr_vessel_count, curr_vessel_count + 5):
#obst_speed = np.random.random()
ship = Vessel(self.config,
width=self.config["vessel_width"],
index=i,
vessel_pos=self.main_vessel.position)
self.moving_obstacles.append(ship)
print(f'Ship {i} has been created')
self._vessel_count += 1
for ship in self.moving_obstacles:
other_ships = [
x for x in self.moving_obstacles if x.index != ship.index
]
#ship.obstacles.extend(other_ships)
ship.obstacles = np.hstack(
[self.static_obstacles, other_ships])
if len(self.queued_vessels) == 0:
self.queued_vessels = [
x for x in self.moving_obstacles if x.index != 0
]
next_vessel = self.main_vessel
else:
next_vessel = self.queued_vessels[0]
obs = next_vessel.observe()
self.t_step += 1
print('EXITIG STEP')
return (obs, reward, done, info)
# [obst.update(self.config["t_step_size"]) for obst in self.moving_obstacles if obst.index != 0]
#
#
# action[0] = (action[0] + 1)/2 # Done to be compatible with RL algorithms that require symmetric action spaces
# if np.isnan(action).any(): action = np.zeros(action.shape)
# self.main_vessel.step(action)
#
#
# # Getting observation vector
# obs = self.observe()
# vessel_data = self.main_vessel.req_latest_data()
# self.collision = vessel_data['collision']
# self.reached_goal = vessel_data['reached_goal']
# self.progress = vessel_data['progress']
#
# # Receiving agent's reward
# reward = self.rewarder.calculate()
# self.last_reward = reward
# self.cumulative_reward += reward
#
# info = {}
# info['collision'] = self.collision
# info['reached_goal'] = self.reached_goal
# info['progress'] = self.progress
#
# # Testing criteria for ending the episode
# done = self._isdone()
#
# self._save_latest_step()
# # If the environment is dynamic, calling self.update will change it.
# self._update()
#
# self.t_step += 1
#
# #Adding moving obstacles (ships)
# if not self.t_step % 100:
# #print(f'Time step: {self.t_step}, position of vessel: {self.main_vessel.position}')
# curr_vessel_count = self._vessel_count
# for i in range(curr_vessel_count ,curr_vessel_count+5):
# #obst_speed = np.random.random()
# ship = Vessel(self.config, width=self.config["vessel_width"], index=i, vessel_pos=self.main_vessel.position)
#
# self.moving_obstacles.append(ship)
# print(f'Ship {i} has been created')
# self._vessel_count += 1
#
#
# for ship in self.moving_obstacles:
# other_ships = [x for x in self.moving_obstacles if x.index != ship.index]
# #ship.obstacles.extend(other_ships)
# ship.obstacles = np.hstack([self.static_obstacles, other_ships])
#
# return (obs, reward, done, info)
def _update(self):
valid_ships = [self.main_vessel]
for ship in self.moving_obstacles:
if (not ship.collision
) and ship.index != 0: # and ship.reachable :
valid_ships.append(ship)
# print(f'Time: {self.t_step}')
print([x.index for x in valid_ships])
self.moving_obstacles = valid_ships
for ship in self.moving_obstacles:
other_ships = [
x for x in self.moving_obstacles if x.index != ship.index
]
#ship.obstacles.extend(other_ships)
ship.obstacles = np.hstack([self.static_obstacles, other_ships])
#print('Exiting UPDATE in MA')
def observe(self):
navigation_states = self.main_vessel.navigate(self.path)
sector_closenesses, sector_velocities, sector_moving_obstacles = self.main_vessel.perceive(
self.obstacles)
obs = np.concatenate([
navigation_states, sector_closenesses, sector_velocities,
sector_moving_obstacles
])
return (obs)
class TwoVessel_HeadOn(BaseEnvironment):
def __init__(self,
env_config,
test_mode=False,
render_mode='2d',
verbose=False):
"""
The __init__ method declares all class atributes and calls
the self.reset() to intialize them properly.
Parameters
----------
env_config : dict
Configuration parameters for the environment.
The default values are set in __init__.py
test_mode : bool
If test_mode is True, the environment will not be autonatically reset
due to too low cumulative reward or too large distance from the path.
render_mode : {'2d', '3d', 'both'}
Whether to use 2d or 3d rendering. 'both' is currently broken.
verbose
Whether to print debugging information.
"""
self.test_mode = test_mode
self.render_mode = render_mode
self.verbose = verbose
self.config = env_config
# Setting dimension of observation vector
self.n_observations = len(
Vessel.NAVIGATION_FEATURES
) + 3 * self.config["n_sectors"] + ColavRewarder.N_INSIGHTS
self.episode = 0
self.total_t_steps = 0
self.t_step = 0
self.history = []
# Declaring attributes
#self.obstacles = None
self.main_vessel = None
#self.agent = None
#self.path = None
self.reached_goal = None
self.collision = None
self.progress = None
self.cumulative_reward = None
self.last_reward = None
self.last_episode = None
self.rng = None
self._tmp_storage = None
self._action_space = gym.spaces.Box(low=np.array([-1, -1]),
high=np.array([1, 1]),
dtype=np.float32)
self._observation_space = gym.spaces.Box(
low=np.array([-1] * self.n_observations),
high=np.array([1] * self.n_observations),
dtype=np.float32)
# Initializing rendering
self._viewer2d = None
self._viewer3d = None
if self.render_mode == '2d' or self.render_mode == 'both':
render2d.init_env_viewer(self)
if self.render_mode == '3d' or self.render_mode == 'both':
render3d.init_env_viewer(self,
autocamera=self.config["autocamera3d"])
# self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/logs/agents/MovingObstacles-v0/1589625657ppo/6547288.pkl')
#self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/logs/agents/MovingObstacles-v0/1590746004ppo/2927552.pkl')
# self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/logs/agents/MovingObstacles-v0/1590827849ppo/4070808.pkl')
#'C:/Users/amalih/OneDrive - NTNU/github/logs/agents/MultiAgentPPO-v0/1064190.pkl'
#self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/logs/agents/MovingObstacles-v0/1590705511ppo/4425456.pkl')
#self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/gym-auv-master/logs/agents/MovingObstacles-v0/1589130704ppo/6916896.pkl')
#self.agent = PPO2.load('C:/Users/amalih/Documents/gym-auv-master/gym-auv-master/logs/agents/MovingObstacles-v0/1589031909ppo/1760568.pkl')
self.agent = PPO2.load(
'C:/Users/amalih/OneDrive - NTNU/github/logs/agents/MultiAgentPPO-v0/1591171914ppo/79288.pkl'
)
self.rewarder_dict = {}
self.reset()
print('Init done')
def _generate(self):
waypoints1 = np.vstack([[0, 0], [0, 500]]).T
path1 = Path(waypoints1)
init_pos1 = path1(0)
init_angle1 = path1.get_direction(0)
init_state1 = np.hstack([init_pos1, init_angle1])
self.main_vessel = Vessel(self.config,
init_state=init_state1,
init_path=path1,
width=2) #self.config["vessel_width"])
self.main_vessel.path = path1
self.rewarder_dict[self.main_vessel.index] = ColavRewarder(
self.main_vessel)
self.rewarder = self.rewarder_dict[self.main_vessel.index]
prog = 0
self.path_prog_hist = np.array([prog])
self.max_path_prog = prog
self.moving_obstacles = [self.main_vessel]
#Adding moving obstacle
waypoints2 = np.vstack([[0, 150], [0, -400]]).T
path2 = Path(waypoints2)
init_pos2 = path2(0)
init_angle2 = path2.get_direction(0)
init_state2 = np.hstack([init_pos2, init_angle2])
vessel = Vessel(self.config,
init_state=init_state2,
init_path=path2,
index=1,
width=2) #self.config["vessel_width"])
self.rewarder_dict[vessel.index] = ColavRewarder(vessel)
self.moving_obstacles.append(vessel)
vessel.path = path2
for vessel in self.moving_obstacles:
other_vessels = [
x for x in self.moving_obstacles if x.index != vessel.index
]
vessel.obstacles = np.hstack([other_vessels])
print('Generated vessels!')
#self._update()
@property
def path(self):
return self.main_vessel.path
@property
def obstacles(self):
return self.main_vessel.obstacles
@property
def vessel(self):
return self.main_vessel
def step(self, action: list) -> (np.ndarray, float, bool, dict):
"""
Steps the environment by one timestep. Returns observation, reward, done, info.
Parameters
----------
action : np.ndarray
[thrust_input, torque_input].
Returns
-------
obs : np.ndarray
Observation of the environment after action is performed.
reward : double
The reward for performing action at his timestep.
done : bool
If True the episode is ended, due to either a collision or having reached the goal position.
info : dict
Dictionary with data used for reporting or debugging
"""
action[0] = (
action[0] + 1
) / 2 # Done to be compatible with RL algorithms that require symmetric action spaces
if np.isnan(action).any(): action = np.zeros(action.shape)
self.main_vessel.step(action)
for vessel in self.moving_obstacles:
if vessel.index != 0:
obs = vessel.observe()
reward = self.rewarder_dict[vessel.index].calculate()
insight = self.rewarder_dict[vessel.index].insight()
#print(f'Reward for vessel {vessel.index}: {reward} -- lambda: {insight}')
obs = np.concatenate([insight, obs])
action, _states = self.agent.predict(obs, deterministic=True)
action[0] = (action[0] + 1) / 2
vessel.step(action)
# Testing criteria for ending the episode
done = self._isdone()
self._save_latest_step()
# Getting observation vector
obs = self.observe()
vessel_data = self.main_vessel.req_latest_data()
self.collision = vessel_data['collision']
self.reached_goal = vessel_data['reached_goal']
self.progress = vessel_data['progress']
# Receiving agent's reward
reward = self.rewarder.calculate()
self.last_reward = reward
#self.cumulative_reward += reward
info = {}
info['collision'] = self.collision
info['reached_goal'] = self.reached_goal
info['progress'] = self.progress
self.t_step += 1
return (obs, reward, done, info)
