params = read_inp()
for i in params:
print(i, params[i])
DEFAULT_ENV_NAME = params['DEFAULT_ENV_NAME'] #"1k43"
MEAN_REWARD_BOUND = eval(params['MEAN_REWARD_BOUND']) #-3.0
RENDER = eval(params['RENDER']) #0
FCOUNTS = eval(params['FCOUNTS']) #10
BCOUNT = eval(params['BCOUNT']) #-1
TRACK = eval(
params['TRACK']
) #5 # how much residue coordinates be included from generated sequence
env = environ_grid('1k43.pdb', DEFAULT_ENV_NAME, RENDER, 0, TRACK, FCOUNTS,
BCOUNT)
GAMMA = eval(params['GAMMA']) #0.99
BATCH_SIZE = eval(params['BATCH_SIZE']) #32
REPLAY_SIZE = eval(params['REPLAY_SIZE']) #10000
LEARNING_RATE = eval(params['LEARNING_RATE']) #1e-4
SYNC_TARGET_FRAMES = eval(params['SYNC_TARGET_FRAMES']) #1000
REPLAY_START_SIZE = eval(params['REPLAY_START_SIZE']) #10000
EPSILON_DECAY_LAST_FRAME = eval(params['EPSILON_DECAY_LAST_FRAME']) #10**6
EPSILON_START = eval(params['EPSILON_START']) #1.0
EPSILON_FINAL = eval(params['EPSILON_FINAL']) #0.05
MAX_ITER = eval(params['MAX_ITER']) #10**9
Experience = collections.namedtuple(
# we don't want to back-propagate through this calculation
# because it is just observations that we want to be true
# That is, we want to change the expected values output from
# the net, not the observations calculation
next_state_values = next_state_values.detach()
# calc the Q function behavior we want
expected_state_action_values = next_state_values * GAMMA + rewards_v
# compare what we have to what we want
return nn.MSELoss()(state_action_values, expected_state_action_values)
DEFAULT_ENV_NAME = "Protein folding"
env = environ_grid('1k43.pdb',DEFAULT_ENV_NAME, 0)
print (env)
obs_size = env.obs_size
n_actions = env.n_actions
print(obs_size,n_actions)
class Net(nn.Module):
def __init__(self, obs_size, hidden_size, n_actions):
super(Net, self).__init__()
def init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_uniform(m.weight)
m.bias.data.fill_(0.0)
DEFAULT_ENV_NAME = '1k43'
pdb = '1k43.pdb'
if len(sys.argv) > 2:
pdb = sys.argv[2]
RENDER = 1
test = 1
DEFAULT_ENV_NAME = params['DEFAULT_ENV_NAME']
FCOUNTS = eval(params['FCOUNTS']) #10
BCOUNT = eval(params['BCOUNT']) #-1
TRACK = eval(params['TRACK']) #5
HIDDEN_SIZE = eval(params['HIDDEN_SIZE'])
env = environ_grid(pdb, DEFAULT_ENV_NAME, RENDER, test, TRACK, FCOUNTS, BCOUNT)
state = env.reset()
total_reward = 0.0
c = collections.Counter()
RENDER = 1
#import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
test_net = DQN(env.obs_size, HIDDEN_SIZE, env.n_actions)
print(test_net)
test_net.load_state_dict(
torch.load("models/" + DEFAULT_ENV_NAME + "-best.dat",
map_location=lambda storage, loc: storage))
while True:
RENDER = 1
test = 1
if RENDER:
os.system('rm -rf temp_grid.npy')
DEFAULT_ENV_NAME = "Protein folding"
device = "cpu"
if len(sys.argv) > 1:
pdb = sys.argv[1]
else:
pdb = '1k43.pdb'
env = environ_grid(pdb, DEFAULT_ENV_NAME, RENDER, test)
print(env)
class Net(nn.Module):
def __init__(self, obs_size, hidden_size, n_actions):
super(Net, self).__init__()
self.net = nn.Sequential(
nn.Linear(obs_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, int(hidden_size / 2)),
#nn.ReLU(),
#nn.Linear(int(hidden_size/2), int(hidden_size/4)),
nn.ReLU(),
nn.Linear(int(hidden_size / 2), n_actions))