def _test_environments():
import environments
from environments.env_maze import EnvMaze
char_grid = [
['T', '.', '.', '.', '.'],
['.', '.', '.', '.', '.'],
['.', '.', '.', '.', '.'],
['W', 'W', '.', 'W', 'W'],
['.', '.', '.', '.', '.'],
['.', '.', '.', '.', '.'],
]
stochasticity = 0.1
bump_penalty = -10
maze = EnvMaze(char_grid, stochasticity, bump_penalty)
maze.reset()
print(maze.stateString())
maze.performAction('LEFT')
print(maze.stateString())
maze.performAction('UP')
print(maze.stateString())
from get_environment_from_name import get_environment_from_name
names = ['FlipCoin', 'FlipTwoCoins', 'Grid', 'Maze']
for env_name in names:
print('____________________________________________')
print(env_name)
env = get_environment_from_name(env_name)
is_finished = env.isFinished()
print(is_finished)
env.reset()
print(env)
action = 0
env.performAction(action)
print('')
print(env)
reward = env.getReward()
print(reward)
is_finished = env.isFinished()
print(is_finished)
if env_name == 'Grid':
env.performAction('UP')
learning_curves[agent_name] = mean_curves
return learning_curves
if __name__ == '__main__':
import sys
if (len(sys.argv) < 2):
env_name = 'Maze'
else:
env_name = sys.argv[1]
# Initialize an environment
from get_environment_from_name import get_environment_from_name
env = get_environment_from_name(env_name)
n_states = env.numStates()
n_actions = env.numActions()
# Make a dictionary, and fill it with agents
agents_ = {}
# from agents.agent_random import AgentRandom
# agents_['Random'] = AgentRandom(n_states, n_actions)
# from agents.agent_first_action import AgentFirstAction
# agents_['FirstAction'] = AgentFirstAction(n_states, n_actions)
from agents.agent_montecarlo_statevalues import AgentMonteCarloV
agents_['MonteCarlo()'] = AgentMonteCarloV(n_states, n_actions)
# for alpha in np.linspace(0.01, 0.3, num=3):
