class HumanAgent():
def __init__(self):
self.train = True
self.keyboard_logger = KeyLogger()
def train(self, _obs):
self.action = self.keyboard_logger.actions_step()
return self.action
def __init__(self, env):
self.controlling = True
self.logging = True
#self.mouse_logger = MouseLogger()
#self.mouse_controller = MouseController()
self.keyboard_logger = KeyLogger()
#self.keyboard_controller = KeyboardController()
self.env = env
self.viewer = None
self.info = None
self.reward = None
self.done = False
self.state = None
#self.action_dim = 3
#self.state_dim = 109
self.num_envs = 1
self.num_envs_per_sub_batch = 1
self.total_pips = []
#self.player = self.env.player
#self.pips = self.env.pips
#self.starter = 0
# forward or backward in each dimension
#self.action_space = spaces.Discrete(3)
self.action_space = self.env.action_space
# observation is the x, y coordinate of the grid
#low = np.zeros(0, dtype=int)
#high = np.array(1, dtype=int) - np.ones(len(self.maze_size), dtype=int)
#self.observation_space = spaces.Box(low=-100000, high=100000, shape=(109,))
self.observation_space = self.env.observation_space
#print("obs")
#print (self.observation_space)
# initial condition
#self.state = self.env.generate_number()
self.steps_beyond_done = None
# Simulation related variables.
self.seed()
#self.reset()
# Just need to initialize the relevant attributes
self.configure()
class Controller_Gym(gym.Env):
metadata = {
"render.modes": ["human", "rgb_array"],
}
#Define Actions
ACTION = [0, 1]
def __init__(self, env):
self.controlling = True
self.logging = True
#self.mouse_logger = MouseLogger()
#self.mouse_controller = MouseController()
self.keyboard_logger = KeyLogger()
#self.keyboard_controller = KeyboardController()
self.env = env
self.viewer = None
self.info = None
self.reward = None
self.done = False
self.state = None
#self.action_dim = 3
#self.state_dim = 109
self.num_envs = 1
self.num_envs_per_sub_batch = 1
self.total_pips = []
#self.player = self.env.player
#self.pips = self.env.pips
#self.starter = 0
# forward or backward in each dimension
#self.action_space = spaces.Discrete(3)
self.action_space = self.env.action_space
# observation is the x, y coordinate of the grid
#low = np.zeros(0, dtype=int)
#high = np.array(1, dtype=int) - np.ones(len(self.maze_size), dtype=int)
#self.observation_space = spaces.Box(low=-100000, high=100000, shape=(109,))
self.observation_space = self.env.observation_space
#print("obs")
#print (self.observation_space)
# initial condition
#self.state = self.env.generate_number()
self.steps_beyond_done = None
# Simulation related variables.
self.seed()
#self.reset()
# Just need to initialize the relevant attributes
self.configure()
def __del__(self):
pass
def configure(self, display=None):
self.display = display
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
#self.state = self.env.generate_number()
#self.env.display()
#print(action)
action = self.keyboard_logger.actions()
#action = 1
#self.placement = self.env.placement
self.next_state, self.reward, self.done, info = self.env.step(action)
#self.info = 0
#print(self.reward)
self.info = {'pnl': 1, 'nav': 1, 'costs': 1}
#self.next_state = self.next_state.tolist()
#self.total_pips.append(self.pips)
if self.done:
pass
return self.next_state, self.reward, self.done, info
def reset(self):
self.state = self.env.reset()
#self.reward = np.array([reward])
#self.state = self.state.tolist()
#self.state = np.array([self.state])
#self.steps_beyond_done = None
self.done = False
#self.done = np.array([self.done])
return self.state
def is_game_over(self):
pass
return
def render(self, mode="human", close=False):
self.env.render()
#self.env.display()
pass
return
def __init__(self):
self.train = True
self.keyboard_logger = KeyLogger()