critic = CriticNet(state_dim, action_dim, HIDDEN1_UNITS, HIDDEN2_UNITS,
HIDDEN2_UNITS, action_dim)
buff = Memory(BUFFER_SIZE, 9)
step = 0
reward_result = []
for i in range(MAX_EPISODES):
s_t = env.reset()
s_t = np.reshape(s_t, (1, 3))[0]
total_reward = 0.
for j in range(MAX_EP_STEPS):
loss = 0
if RENDER_ENV:
env.render()
a_t = actor.predict(s_t, ACTION_BOUND, target=False)
action = a_t + ou.sample(a_t[0])
s_t_1, r_t, done, info = env.step(action)
buff.store(s_t, a_t[0], r_t, np.reshape(s_t_1, (1, 3))[0], [done])
if buff.t_memory > MINIBATCH_SIZE:
batch = buff.sample(MINIBATCH_SIZE)
states_t = batch[:, 0:3]
actions = batch[:, 3]
rewards = batch[:, 4]
b_s_ = batch[:, 5:8]
dones = batch[:, -1]
y = np.zeros((len(batch), 1))
a_tgt = actor.predict(b_s_, ACTION_BOUND, target=True)
Q_tgt = critic.predict(b_s_, a_tgt, target=True)
for i in range(len(batch)):
if dones[i]:
for times in range(max_time):
step=0
x=np.linspace(1,MAX_EPISODES,MAX_EPISODES)
for ii in range(MAX_EPISODES):
s_t = env.reset()
total_reward = 0.
count=0
for j in range(MAX_EP_STEPS):
loss=0;
loss2 = 0;
if RENDER_ENV:
env.render()
a_t = actor.predict(np.reshape(s_t,(1,3)), ACTION_BOUND, target=False)+1./(1.+ii+j)
s_t_1, r_t, done, info = env.step(a_t[0])
buff.add(s_t, a_t[0], r_t, s_t_1, done)
if buff.count() > MINIBATCH_SIZE:
batch = buff.getBatch(MINIBATCH_SIZE)
states_t = np.asarray([e[0] for e in batch])
actions = np.asarray([e[1] for e in batch])
rewards = np.asarray([e[2] for e in batch])
states_t_1 = np.asarray([e[3] for e in batch])
dones = np.asarray([e[4] for e in batch])
y=np.zeros((len(batch), action_dim))
a_tgt=actor.predict(states_t_1, ACTION_BOUND, target=True)
Q_tgt = critic.predict(states_t_1, a_tgt,target=True)
for i in range(len(batch)):
if dones[i]:
