def test_taking_actions():
"""Does the environment correctly change the state when told to take an action with and without stochasticity?"""
random.seed()
env = GridworldContinuous(0.05, 0.01)
start = env.initial_state()
ratio = ratio_test(lambda state: np.linalg.norm(np.asarray([state[0] - start[0], state[1] - (start[1] + env.move_mean)]), 2) < env.move_sd * 2,
partial(env.next_state, start, Action.up), 10000)
assert 0.7 < ratio
steps = 0
s = env.initial_state()
while not env.is_terminal(s):
s = env.next_state(s, np.random.randint(4))
steps += 1
assert steps < 20000
def main(argv):
"""Execute experiment."""
args = parse_args(argv)
random.seed(args.seed)
logging.basicConfig(level=log_level(args.log_level))
environment = (args.environment == 'gridworld' and Gridworld(args.gridworld_width, args.gridworld_height, args.failure_rate)) or \
(args.environment == 'gridworld_continuous' and GridworldContinuous(0.2, 0.01)) or \
(args.environment == 'combo_lock' and CombinationLock(args.gridworld_height, args.gridworld_width, 4, args.failure_rate))
policy = (args.agent_policy == 'random'
and RandomPolicy(environment.num_actions))
fa = ((args.environment == 'gridworld' or args.environment == 'combo_lock') and
TabularFA(environment.num_states(), environment.num_actions)) or \
(args.environment == 'gridworld_continuous' and RBF(2, 5, environment.num_actions, beta=args.beta))
agent = Agent(policy,
fa,
environment.num_actions,
args.alpha,
args.gamma,
args.eta,
args.zeta,
args.epsilon,
args.num_vi,
args.sim_samples,
args.sim_steps,
retain_theta=args.retain_theta,
subgoals=environment.create_subgoals())
agent.policy = RandomOptionPolicy(agent, args.random_options)
if args.agent_viz:
agent.create_visualization(
args.environment == 'gridworld'
or args.environment == 'combo_lock', environment)
# agent.exploit(np.asarray([1, 1]))
# agent.exploit(environment.num_states()-1)
# results_descriptor = ResultsDescriptor(args.results_interval, args.results_path, ['interval_id', 'steps'])
# experiment_descriptor = ExperimentDescriptor(args.plan_interval, args.num_steps)
# start(experiment_descriptor, agent, environment, results_descriptor)
e = Experiment2(agent, environment, args.plan_interval, args.num_steps,
args.viz_steps)
e.run()
def test_taking_actions():
"""Does the environment correctly change the state when told to take an action with and without stochasticity?"""
random.seed()
env = GridworldContinuous(0.05, 0.01)
start = env.initial_state()
ratio = ratio_test(
lambda state: np.linalg.norm(
np.asarray(
[state[0] - start[0], state[1] -
(start[1] + env.move_mean)]), 2) < env.move_sd * 2,
partial(env.next_state, start, Action.up), 10000)
assert 0.7 < ratio
steps = 0
s = env.initial_state()
while not env.is_terminal(s):
s = env.next_state(s, np.random.randint(4))
steps += 1
assert steps < 20000
def test_termination():
"""Does the environment terminate in the correct state?"""
env = GridworldContinuous(0.05, 0.01)
assert not env.is_terminal(GridPosition(0, 0))
assert env.is_terminal(GridPosition(0.97, 0.98))
def test_termination():
"""Does the environment terminate in the correct state?"""
env = GridworldContinuous(0.05, 0.01)
assert not env.is_terminal(GridPosition(0, 0))
assert env.is_terminal(GridPosition(0.97, 0.98))
