def plot_grid_2_mc():
test_grids = TEST_GRIDS
all_test_list = [(key, grid) for key, grid in test_grids.items()]
sorted(all_test_list, key=lambda x: x[0])
agent = Agent()
iters = ITERS
total_normal_grid_score, total_grid1_score, total_grid2_score, total_grid3_score, total_grid4_score = [],[],[],[],[]
repeats = REPEATS
# for n in iters:
# print("Running iteration {n}".format(n=n))
grid2_score, grid4_score = [], []
for ind, grid_init in all_test_list:
normalized_score = 0
for j in range(repeats):
grid_num = int(ind) #ind initially is a string.
if (grid_num < 200) or (grid_num > 300):
continue
best_reward = grid_init['best_reward']
testgrid = Grid(5, random=False, init_pos=grid_init)
if grid_num in {204, 208}:
Q, policy = agent.mc_first_visit_control(testgrid.copy(),
iters=500)
_, _, mc_reward = agent.run_final_policy(testgrid.copy(),
Q,
display=True)
else:
continue
normalized_score += mc_reward - best_reward
if normalized_score != 0:
print(
"Grid num {0} did not achieve best score".format(grid_num))
def graph_dual_model_performance():
test_grids = TEST_GRIDS
all_test_list = [(key, grid) for key, grid in test_grids.items()]
sorted(all_test_list, key=lambda x: x[0])
agent = Agent()
iters = ITERS
total_normal_grid_score, total_grid1_score, total_grid2_score, total_grid3_score, total_grid4_score = [],[],[],[],[]
repeats = REPEATS
for n in iters:
print("Running iteration {n}".format(n=n))
normal_grid_score, grid1_score, grid2_score, grid3_score, grid4_score = [],[],[],[],[]
for ind, grid_init in all_test_list:
normalized_score = 0
for j in range(repeats):
grid_num = int(ind) #ind initially is a string.
best_reward = grid_init['best_reward']
testgrid = Grid(5, random=False, init_pos=grid_init)
Q, policy = agent.mc_first_visit_control(testgrid.copy(),
iters=n,
nn_init=True)
_, _, dual_model_reward = agent.run_final_policy(
testgrid.copy(), Q, nn_init=True, display=False)
normalized_score += dual_model_reward - best_reward
if grid_num < 100:
normal_grid_score.append(normalized_score / repeats)
elif grid_num < 200: #grid type 1
grid1_score.append(normalized_score / repeats)
elif grid_num < 300: #grid type 2
grid2_score.append(normalized_score / repeats)
elif grid_num < 400: #grid type 3
grid3_score.append(normalized_score / repeats)
else: #grid type 4
grid4_score.append(normalized_score / repeats)
total_normal_grid_score.append(np.mean(normal_grid_score))
total_grid1_score.append(np.mean(grid1_score))
total_grid2_score.append(np.mean(grid2_score))
total_grid3_score.append(np.mean(grid3_score))
total_grid4_score.append(np.mean(grid4_score))
# plt.plot(iters, total_normal_grid_score, label="normal grids", color="red")
plt.plot(iters, total_grid1_score, label='push dilemma', color="blue")
plt.plot(iters, total_grid2_score, label='switch dilemma', color="green")
plt.plot(iters, total_grid3_score, label='switch save', color="orange")
plt.plot(iters, total_grid4_score, label='push get', color="brown")
plt.legend()
plt.xlabel("Number of MC Iterations")
plt.ylabel("Normalized Score")
plt.title("Dual model performance on all test grids")
plt.show()
