def run_experiment(intrinsic_motivation, gamma, c, mc_n, runs, steps):
trajectories = []
start = np.array([50, 50])
true_belief = True
for _ in range(runs):
goal = draw_goal(start, 6)
manual = draw_goal(start, 3)
print("Goal: {}".format(goal))
print("Manual: {}".format(manual))
world = state.ToyWorld([100, 100], intrinsic_motivation, goal, manual)
belief = None
if true_belief:
belief = dict(
zip(
[
state.ToyWorldAction(np.array([0, 1])),
state.ToyWorldAction(np.array([0, -1])),
state.ToyWorldAction(np.array([1, 0])),
state.ToyWorldAction(np.array([-1, 0])),
],
[[10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10]],
)
)
root_state = state.ToyWorldState(start, world, belief=belief)
print(root_state.pos)
next_state = StateNode(None, root_state, 0)
trajectory = []
for _ in range(steps):
try:
ba = mcts_search(next_state, gamma, c=c, n=mc_n)
print("")
print("=" * 80)
print("State: {}".format(next_state.state))
print("Belief: {}".format(next_state.state.belief))
print("Reward: {}".format(next_state.reward))
print("N: {}".format(next_state.n))
print("Q: {}".format(next_state.q))
print("Action: {}".format(ba.action))
trajectory.append(next_state.state.pos)
if (next_state.state.pos == np.array(goal)).all():
break
next_s = next_state.children[ba].sample_state(real_world=True)
next_state = next_s
next_state.parent = None
except KeyboardInterrupt:
break
trajectories.append(trajectory)
with open(gen_name("trajectories", "pkl"), "w") as f:
pickle.dump(trajectories, f)
print("=" * 80)
def run_experiment(intrinsic_motivation, gamma, c, mc_n, runs, steps):
trajectories = []
start = np.array([50, 50])
true_belief = True
for _ in range(runs):
goal = draw_goal(start, 6)
manual = draw_goal(start, 3)
print("Goal: {}".format(goal))
print("Manual: {}".format(manual))
world = state.ToyWorld([100, 100], intrinsic_motivation, goal, manual)
belief = None
if true_belief:
belief = dict(
zip([
state.ToyWorldAction(np.array([0, 1])),
state.ToyWorldAction(np.array([0, -1])),
state.ToyWorldAction(np.array([1, 0])),
state.ToyWorldAction(np.array([-1, 0]))
], [[10, 10, 10, 10], [10, 10, 10, 10], [10, 10, 10, 10],
[10, 10, 10, 10]]))
root_state = state.ToyWorldState(start, world, belief=belief)
print(root_state.pos)
next_state = StateNode(None, root_state, 0)
trajectory = []
for _ in range(steps):
try:
ba = mcts_search(next_state, gamma, c=c, n=mc_n)
print("")
print("=" * 80)
print("State: {}".format(next_state.state))
print("Belief: {}".format(next_state.state.belief))
print("Reward: {}".format(next_state.reward))
print("N: {}".format(next_state.n))
print("Q: {}".format(next_state.q))
print("Action: {}".format(ba.action))
trajectory.append(next_state.state.pos)
if (next_state.state.pos == np.array(goal)).all():
break
next_s = next_state.children[ba].sample_state(real_world=True)
next_state = next_s
next_state.parent = None
except KeyboardInterrupt:
break
trajectories.append(trajectory)
with open(gen_name("trajectories", "pkl"), "w") as f:
pickle.dump(trajectories, f)
print("=" * 80)
def run_experiment(intrinsic_motivation, gamma, c, mc_n, runs, steps, problem):
st1 = time.time()
# trajectories = []
start = np.array([50, 50])
true_belief = True
mcts_search = MCTS(tree_policy=UCB1(c=1.41),
default_policy=immediate_reward,
backup=monte_carlo)
rewards = []
for r in range(runs):
sta = time.time()
print("RUN number", r)
goal = draw_goal(start, 6)
# manual = draw_goal(start, 3)
# print("Goal: {}".format(goal))
world = PaintingWorld((100, 100), False, (100, 100), problem)
belief = None
root_state = PaintingWorldState((0, 0), (1, 1, 1), world)
if true_belief:
belief = {}
for action in root_state.actions:
belief[action] = [1] * len(root_state.actions)
root_state.belief = belief
# print(root_state.pos)
next_state = StateNode(None, root_state)
# trajectory =[]
rew = 0
for step in range(steps):
st = time.time()
ba = mcts_search(next_state, n=mc_n)
# print("=" * 80)
# print("State: {}".format(next_state.state))
# # print("Belief: {}".format(next_state.state.belief))
# print("Reward: {}".format(next_state.reward))
# print("N: {}".format(next_state.n))
# print("Q: {}".format(next_state.q))
# print("Action: {}".format(ba.action))
# trajectory.append(next_state.state.pos)
rew = next_state.reward
if (next_state.state.pos == np.array(goal)).all():
break
next_s = next_state.children[ba].sample_state(real_world=True)
next_state = next_s
next_state.parent = None
en = time.time()
print("step", step, "time elapsed", en - st)
if step >= 5 and rew > 0.5:
break
# except KeyboardInterrupt:
# break
# trajectories.append(trajectory)
# print (next_state.reward)
rewards.append(rew)
# with open(gen_name("trajectories", "pkl"), "w") as f:
# pickle.dump(trajectories, f)
# print("=" * 80)
end = time.time()
print("run", r, "time elapsed", end - sta)
# if rewards[-1] > 0:
# break
w = max(rewards)
print("REWARD", w)
end1 = time.time()
print("problem time elapsed", end1 - st1)
return w
