if done: # reset game and
state = env.reset()
all_rewards.append((frame_idx, episode_reward))
episode_reward = 0
if len(
replay_buffer
) > replay_initial: #if number of plays has reached the limit calculate loss and optimize update model
loss = compute_td_loss(model, target_model, batch_size, gamma,
replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(
replay_buffer) <= replay_initial: #two ifs are just for printing
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
if frame_idx % 50000 == 0:
target_model.copy_from(model) #updates target model
print("saved modelRe")
torch.save(model.state_dict(), "modelRe.pth")
savetxt('rewards.csv', all_rewards, delimiter=',')
savetxt('losses.csv', losses, delimiter=',')
if done:
state = env.reset()
all_rewards.append(episode_reward)
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.cpu().numpy())
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: {}, preparing replay buffer'.format(frame_idx))
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: {}, Loss: {}'.format(frame_idx, np.mean(losses)))
print('Last-10 average reward: {}'.format(np.mean(all_rewards[-10:])))
if frame_idx % 500000 == 0:
checkpoint = {
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'frame_idx': frame_idx,
'losses': losses,
'all_rewards': all_rewards,
# 'replay_buffer' : replay_buffer
}
torch.save(checkpoint, "./checkpoints/{}_checkpoint".format(frame_idx))
episode_reward = 0 # reset
# Once the replay buffer has filled up enough
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(
model, target_model, batch_size, gamma,
replay_buffer) # calculate the loss for the state
optimizer.zero_grad() # reset gradient values
loss.backward() # backpropogate loss
optimizer.step() # Updates weight values
losses.append(
(frame_idx, loss.data.cpu().numpy())) # hold loss in array
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
torch.save(model.state_dict(), "run11_start.pth")
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
filename = "run11_model" + str(frame_idx) + ".pth"
torch.save(model.state_dict(), filename)
lossScript = open("run11_losses.txt", "w")
rewardScript = open("run11_rewards.txt", "w")
lossScript.write(str(losses))
rewardScript.write(str(all_rewards))
lossScript.close()
rewardScript.close()
if frame_idx % 50000 == 0:
target_model.copy_from(
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, target_model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
if (np.mean(all_rewards[-10:], 0)[1] > currMax):
print("Saving model...")
torch.save(model.state_dict(), filename)
currMax = np.mean(all_rewards[-10:], 0)[1]
else:
print("not saving")
if frame_idx % 20000 == 0:
print("Copying from model...")
target_model.copy_from(model)
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, target_model, batch_size, gamma,
replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
times.append(frame_idx)
value_losses.append(np.mean(losses, 0)[1])
value_all_rewards.append(np.mean(all_rewards[-10:], 0)[1])
if frame_idx % 50000 == 0:
target_model.copy_from(model)
# save losses and rewards to txt
open('data_losses.txt', 'w').write(str(losses))
open('data_all_rewards.txt', 'w').write(str(all_rewards))
# draw two plots
draw_plot()
# save the trained model
torch.save(model.state_dict(), "trained_model_revised.pth")
state = next_state # Change to next state
episode_reward += reward # Keep adding rewards until goal state
if done: # Goal state
state = env.reset() # Restart game
all_rewards.append((frame_idx, episode_reward)) # Store episode_reward w frame it ended
episode_reward = 0
if len(replay_buffer) > replay_initial: # If enough frames in replay_buffer (10000)
loss = compute_td_loss(model, target_model, batch_size, gamma, replay_buffer)
optimizer.zero_grad() # Resets gradient after every mini-batch
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0:
if len(replay_buffer) <= replay_initial: # If frames still needed in replay_buffer
print('#Frame: %d, preparing replay buffer' % frame_idx)
else: # If enough frames in replay_buffer
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
plot_model(losses, all_rewards)
if best_mean_reward < np.mean(all_rewards[-10:], 0)[1]:
best_mean_reward = np.mean(all_rewards[-10:], 0)[1]
torch.save(model.state_dict(), f"models/model_r={best_mean_reward}_f={frame_idx}.pth")
if frame_idx % sync_models_at_frame == 0:
target_model.copy_from(model) # Copy model's weights onto target after number of frames
batch_size = 32
gamma = 0.99
target_update = 50000
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 1000000
replay_initial = 10000
learning_rate = 1e-5
train_replay_buffer = ReplayBuffer(100000)
analysis_replay_buffer = ReplayBuffer(100000)
policy_model = QLearner(env, train_num_frames, batch_size, gamma,
train_replay_buffer)
target_model = QLearner(env, train_num_frames, batch_size, gamma,
train_replay_buffer)
target_model.load_state_dict(policy_model.state_dict())
target_model.eval()
optimizer = optim.Adam(policy_model.parameters(), lr=learning_rate)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if USE_CUDA:
policy_model = policy_model.to(device)
target_model = target_model.to(device)
epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
def play_to_train(num_frames, policy_model, target_model, buffer):
losses = []
all_rewards = []
with open("rewards.txt", "w") as output:
for row in all_rewards:
output.write(str(row[0]) + "," + str((row[1])) + "\n")
with open("losses.txt", "w") as output:
for row in losses:
output.write(str(row[0]) + "," + str((row[1])) + "\n")
episode_reward = 0
if len(replay_buffer) > replay_initial:
gamma1 = gamma * (10000 / frame_idx)
loss = compute_td_loss(model, target_model, batch_size, gamma1,
replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
torch.save(model.state_dict(), "model2.pth")
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
torch.save(model.state_dict(), "model2.pth")
if frame_idx % 50000 == 0:
target_model.copy_from(model)
state = next_state
episode_reward += reward
if done:
state = env.reset()
all_rewards.append((frame_idx, episode_reward))
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, target_model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
torch.save(model.state_dict(),'my_trained.pth') # saving our model every 10,000 frames
if frame_idx % 50000 == 0:
target_model.copy_from(model)
np.save('losses.npy',losses) #saving the losses and rewards after 2M frames
np.save('rewards.npy',all_rewards)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
if done:
state = env.reset()
all_rewards.append(episode_reward)
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.cpu().numpy())
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
#print(episode_reward)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses)))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:]))
#print(episode_reward)
plot(frame_idx, all_rewards, losses)
torch.save(model.state_dict(), 'newdqnModel.pt')
#torch.save(model, 'entiremodel.pt')
#model.save_model('ndqnModel.tar',model)
loss = compute_td_loss(model, target_model, batch_size, gamma,
replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
if frame_idx % 50000 == 0:
target_model.copy_from(model)
if frame_idx % 1000 == 0:
#PART 4.1
print("frame: ", frame_idx, " - saving new model")
torch.save(model.state_dict(), "new_model.pth")
#PART 4.2
np.save('losses-itr.npy', losses)
np.save('rewards-itr.npy', all_rewards)
#PART 4.2 again if loop finishes.
np.save('losses.npy', losses)
np.save('rewards.npy', all_rewards)
# data = np.load('losses.npy')
# print(data)
# print(np.shape(next_state))
#print(reward)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
if done:
state = env.reset()
all_rewards.append(episode_reward)
#print(episode_reward)
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.cpu().numpy())
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses)))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:]))
pkl.dump(losses, open("loss.pkl", "wb"))
pkl.dump(all_rewards, open("reward.pkl", "wb"))
torch.save(model.state_dict(), 'model_trained.pt')
state = next_state
episode_reward += reward
if done:
state = env.reset()
all_rewards.append((frame_idx, episode_reward))
episode_reward = 0
savetxt('rewards.csv', all_rewards, delimiter=',')
savetxt('losses.csv', losses, delimiter=',')
if len(replay_buffer) > replay_initial:
# We write the loss function
loss = compute_td_loss(model, target_model, batch_size, gamma,
replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append((frame_idx, loss.data.cpu().numpy()))
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses, 0)[1]))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
if frame_idx % 50000 == 0:
target_model.copy_from(model) #update!
torch.save(model.state_dict(), 'modelsave.pth')
episode_reward += reward
if done:
state = env.reset()
all_rewards.append(episode_reward)
episode_reward = 0
if len(replay_buffer) > replay_initial:
loss = compute_td_loss(model, batch_size, gamma, replay_buffer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.cpu().numpy())
if frame_idx % 10000 == 0 and len(replay_buffer) <= replay_initial:
print('#Frame: %d, preparing replay buffer' % frame_idx)
if frame_idx % 10000 == 0 and len(replay_buffer) > replay_initial:
print('#Frame: %d, Loss: %f' % (frame_idx, np.mean(losses)))
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:]))
loss_list.append(np.mean(losses))
reward_list.append(np.mean(all_rewards[-10:]))
sio.savemat('Results.mat', {
'reward_list': reward_list,
'loss_list': loss_list
})
torch.save(model.state_dict(), 'trained_model.pth')
env_id = "PongNoFrameskip-v4"
env = make_atari(env_id)
env = wrap_deepmind(env)
env = wrap_pytorch(env)
num_frames = 1000000
batch_size = 32
gamma = 0.99
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
t_replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
target_model = QLearner(env, num_frames, batch_size, gamma, t_replay_buffer)
target_model.load_state_dict(model.state_dict())
optimizer = optim.Adam(model.parameters(), lr=0.00001)
if USE_CUDA:
model = model.cuda()
target_model = target_model.cuda()
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 30000
epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
losses = []
all_rewards = []
episode_reward = 0
print('Frame: %d' % frame_idx)
print('Loss: %f' % np.mean(losses, 0)[1])
print('Last-10 average reward: %f' % np.mean(all_rewards[-10:], 0)[1])
print('\n\n')
reported_avg_reward = np.mean(all_rewards[-10:], 0)[1]
progress = open('progress_lr0001_epsilon50k.txt', 'a')
progress.write('Frame: %d\n' % (frame_idx))
progress.write('Loss: %f\n' % (np.mean(losses, 0)[1]))
progress.write('Last-10 average reward: %f\n' %
np.mean(all_rewards[-10:], 0)[1])
progress.write('Epsilon: %f\n' % epsilon)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
progress.write('Time: ' + current_time)
progress.write("\n\n")
progress.close()
# EVERY 50K FRAMES, UPDATE TARGET NETWORK WITH CURRENT EXMTL ONE
if frame_idx % 50000 == 0:
target_model.copy_from(model)
# target_model.eval()
file_out = file_name
file_out = re.sub('\.pth', '', file_out)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
file_out = file_out + "_" + current_time + "_" + "epsilon_" + str(
epsilon) + "_frame_" + str(frame_idx) + ".pth"
torch.save(model.state_dict(), file_out)
# ESSENTIALLY, BACKPROPOGATE EVERY 10K FRAMES AND UPDATE THE COMPARISON MODEL AFTER 5 UPDATES