env_id = "PongNoFrameskip-v4" # established environment that will be played
env = make_atari(env_id)
env = wrap_deepmind(env)
env = wrap_pytorch(env)
num_frames = 1000000 # total frames that will be learning from
batch_size = 32 # the number of samples that are provided to the model for update services at a given time
gamma = 0.99 # the discount of future rewards
record_idx = 10000 #
replay_initial = 10000 # number frames that are held
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(
torch.load("model_pretrained.pth",
map_location='cpu')) #loading in the pretrained model
target_model = QLearner(env, num_frames, batch_size, gamma,
replay_buffer) #load in model
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(),
lr=0.0001) #learning rate set and optimizing the model
if USE_CUDA:
model = model.cuda() # sends model to gpu
target_model = target_model.cuda()
print("Using cuda")
epsilon_start = 1.0
epsilon_final = 0.01
import matplotlib.pyplot as plt
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)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load('trained_model.pth'))
model.eval()
if USE_CUDA:
model = model.cuda()
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 30000
losses = []
all_rewards = []
episode_reward = 0
loss_list = []
reward_list = []
from dqn import QLearner, compute_td_loss, ReplayBuffer
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
record_idx = 10000
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load("model_pretrained.pth", map_location='cpu'))
target_model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(), lr=0.00001)
if USE_CUDA:
model = model.cuda()
target_model = target_model.cuda()
print("Using 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)
USE_CUDA = torch.cuda.is_available()
from dqn import QLearner, compute_td_loss, ReplayBuffer
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)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load(sys.argv[1], map_location='cpu'))
model.eval()
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:
epsilon_start = 0.1
epsilon_final = 0.1
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()
if len(sys.argv) > 1:
checkpoint = torch.load(sys.argv[1])
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
#frame_start = checkpoint['frame_idx']
#losses = checkpoint['losses']
#all_rewards = checkpoint['all_rewards']
#replay_buffer = checkpoint['replay_buffer']
frame_start = 1300000
for frame_idx in range(frame_start, frame_start + num_frames + 1):
epsilon = epsilon_by_frame(frame_idx)
# given our state (received from the env), model chooses an action
action = model.act(state, epsilon)
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
record_idx = 10000
filename = "2model.pth"
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load(filename, map_location='cpu'))
target_model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(), lr=0.00001)
if USE_CUDA:
model = model.cuda()
target_model = target_model.cuda()
print("Using 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)
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 = []
from dqn import QLearner, compute_td_loss, ReplayBuffer
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
record_idx = 10000
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load("model1.pth", map_location='cpu'))
target_model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(), lr=0.00001)
if USE_CUDA:
model = model.cuda()
target_model = target_model.cuda()
print("Using 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)
#num_frames = 20000
batch_size = 32
gamma = 0.99
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
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)
"""loading the saved model"""
device = torch.device("cuda")
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
filename = 'newdqnModel.pt'
model.load_state_dict(torch.load(filename))
model.to(device)
model.eval()
"""choosing 1000 frames randomly!"""
frame_range = 50000
frame_list = set(random.sample(range(1, frame_range), 1000))
vis_feature_matrix = []
vis_rewards = []
vis_actions = []
state = env.reset()
indx = 0
episode_reward = 0
for frame_idx in range(0,frame_range):
#print(frame_idx)
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
