class Agent:
def __init__(self, env, config, wt):
self.C = config
self.n_state = list(env.observation_space.shape)
self.n_action = env.action_space.n
self.epsilon = 0.99
self.lr = 1e-3
self.wt = wt
self.buffer = ReplayBuffer(self.C['max_size'], self.C['frame_stack'])
self.buffer2 = ReplayBuffer(self.C['max_size'], self.C['frame_stack'])
self.net = Net(self.n_state, self.n_action, self.C, self.wt)
#Random action during Practice
def act_pre(self):
a = np.random.randint(self.n_action)
return a
#Epsilon greedy action selection function
def act(self, s):
a = self.greedy_act(
s) if np.random.random() > self.epsilon else np.random.randint(
self.n_action)
return a
def greedy_act(self, s):
return self.net.action(s)
#Practice without recording experiences
def practice(self):
self.lr = 1e-3 #possible
self.net.pre_train(self.buffer, self.lr)
#Records experiences and calls training functions
def record(self, s, a, r, d, it, pre):
#Variable pre is used to differentiate practice from RL training.
if pre:
self.buffer.append(s, a, r, d)
if it > self.C['pre_training_start']:
if it % self.C['pre_train_freq'] == 0:
self.lr = 1e-3
self.net.pre_train(self.buffer, self.lr)
else:
self.buffer.append(s, a, r, d)
if it <= 5e5:
self.epsilon = linear_interp(0, 5e5, it, 0.1, 1.0)
else:
self.epsilon = max(linear_interp(5e5, 10e6, it, 0.01, 0.1),
0.01)
if it > self.C['training_start']:
if it % self.C['train_freq'] == 0:
self.lr = 1e-4 #Learning rate for RL training
self.net.train(self.buffer, self.lr)
if it % self.C['update_target_freq'] == 0:
self.net.update_target_network()
class Agent:
def __init__(self, env, config, wt):
self.C = config
self.n_state = list(env.observation_space.shape)
self.n_action = env.action_space.n
self.epsilon = 0.99
self.lr = 1e-3
self.wt = wt
self.buffer = ReplayBuffer(self.C['max_size'], self.C['frame_stack'])
self.buffer2 = ReplayBuffer(self.C['max_size'], self.C['frame_stack'])
self.net = Net(self.n_state, self.n_action, self.C, self.wt)
def act_pre(self):
a = np.random.randint(self.n_action)
return a
def act(self, s):
a = self.greedy_act(
s) if np.random.random() > self.epsilon else np.random.randint(
self.n_action)
return a
def greedy_act(self, s):
return self.net.action(s)
def record(self, s, a, r, d, it, pre):
if pre:
self.buffer.append(s, a, r, d)
if it > self.C['pre_training_start']:
if it % self.C['pre_train_freq'] == 0:
self.lr = 1e-3 #possible
self.net.pre_train(self.buffer, self.lr)
else:
self.buffer.append(s, a, r, d)
if it <= 6e5:
self.epsilon = linear_interp(0, 6e5, it, 0.1, 1.0)
else:
self.epsilon = max(linear_interp(6e5, 10e6, it, 0.01, 0.1),
0.01)
if it > self.C['training_start']:
if it % self.C['train_freq'] == 0:
self.lr = 1e-4
self.net.train(self.buffer, self.lr)
# print(Q)
if it % self.C['update_target_freq'] == 0:
self.net.update_target_network()
