epsilon_decay = 30000 #used in ?
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
state = env.reset() # initial state
for frame_idx in range(1, num_frames +
1): # plays until player or model gets score 21
#print("Frame: " + str(frame_idx)) #uncomment to look at frames
epsilon = epsilon_by_frame(frame_idx)
action = model.act(state, epsilon) #will write this function
next_state, reward, done, _ = env.step(action) #get next state
replay_buffer.push(state, action, reward, next_state,
done) #push actions resutls to buffer
state = next_state
episode_reward += reward
if done: # reset game and
state = env.reset()
all_rewards.append((frame_idx, episode_reward))
episode_reward = 0
if len(
replay_buffer
losses = []
all_rewards = []
episode_reward = 0
state = env.reset()
for frame_idx in range(
1, num_frames + 1
): # QUESTION: Why is num_frames > replay_buffer capacity? replay_buffer should
# overfill because num_frames is larger, so it will keep adding. Does it automatically expand when you push? I think it does expand,
# using the numpy expand_dims funciton
#print("Frame: " + str(frame_idx))
epsilon = epsilon_by_frame(frame_idx) # get the epsilon value
action = model.act(state, epsilon) # This is where act function is used
next_state, reward, done, _ = env.step(
action) # look at next state to see if it gives us a reward
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
# if the game is over
if done:
state = env.reset()
all_rewards.append(
(frame_idx, episode_reward)) # record reward for that game
episode_reward = 0 # reset
frame_list = random.sample(range(2000, num_frames), 8000)
frame_list.sort()
hiddenLayers = []
state_list = []
action_list = []
reward_frame_list = []
accumulated_reward = [] #####
frame_order = []
epsilon = -1
for frame_idx in range(1, num_frames + 1):
action = model.act(state, epsilon)
next_state, reward, done, _ = env.step(action)
if (frame_idx in frame_list) or (frame_idx < 2000):
hiddenTensor = model.get_hidden_layer(state)
temp = hiddenTensor.data.cpu().numpy()
hiddenLayers.append(temp[0])
#hiddenLayers.append(hiddenTensor.data.cpu().numpy())
#hiddenLayers = np.concatenate((hiddenLayers, hiddenTensor.data.cpu().numpy()), axis=0)
state_list.append(state.squeeze(0))
action_list.append(action)
reward_frame_list.append(reward)
accumulated_reward.append(episode_reward) #####
frame_order.append(frame_idx)
#env.env.ale.saveScreenPNG('test_image.png')
if USE_CUDA:
model = model.cuda()
print("Using cuda")
model.load_state_dict(torch.load(pthname, map_location='cpu'))
env.seed(1)
state = env.reset()
done = False
games_won = 0
while not done:
if use_gui:
env.render()
action = model.act(state, 0)
state, reward, done, _ = env.step(action)
if reward != 0:
print(reward)
if reward == 1:
games_won += 1
print("Games Won: {}".format(games_won))
try:
sys.exit(0)
except:
pass
model = model.cuda()
target_model = target_model.cuda()
print("Using cuda")
# Neg exp func. Start exploring then exploiting according to frame_indx
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
state = env.reset() # Initial state
best_mean_reward = float('-inf')
for frame_idx in range(starting_frame, num_frames + 1): # Each frame in # frames played
epsilon = epsilon_by_frame(frame_idx) # Epsilon decreases as frames played
action = model.act(state, epsilon) # if (rand < e) explore. Else action w max(Q-val). action: int
next_state, reward, done, _ = env.step(action) # Get env info after taking action. next_state: 2d int. reward: float. done: bool.
replay_buffer.push(state, action, reward, next_state, done) # Save state info onto buffer (note: every frame)
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
epsilon_final = 0.01 # go towards this from above
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
state = env.reset()
for frame_idx in range(1, num_frames + 1): # play til 21 pts
#print("Frame: " + str(frame_idx))
epsilon = epsilon_by_frame(frame_idx)
action = model.act(state, epsilon) # I write this
next_state, reward, done, _ = env.step(action)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
if done: # game over, save
state = env.reset()
all_rewards.append((frame_idx, episode_reward))
episode_reward = 0
if len(replay_buffer) > replay_initial: # go past replay buffer
loss = compute_td_loss(
model, target_model, batch_size, gamma, replay_buffer