running_state = ZFilter((num_inputs, ), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
episode_lengths = []
optim_epochs = 5
optim_percentage = 0.05
for i_episode in count(1):
ep_memory = Memory_Ep()
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < args.batch_size:
state = env.reset()
state = running_state(state)
policy_net.reset()
reward_sum = 0
memory = Memory()
for t in range(10000): # Don't infinite loop while learning
if args.use_joint_pol_val:
action = select_action_actor_critic(state)
else:
action = select_action(state)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
next_state = running_state(next_state)
mask = 1
expert = Expert(args.expert_path, num_inputs)
print 'Loading expert trajectories ...'
expert.push()
print 'Expert trajectories loaded.'
for i_episode in count(1):
ep_memory = Memory_Ep()
num_steps = 0
reward_batch = 0
true_reward_batch = 0
num_episodes = 0
while num_steps < args.batch_size:
state = env.reset()
#state = running_state(state)
policy_net.reset()
reward_net.reset()
reward_sum = 0
true_reward_sum = 0
memory = Memory()
for t in range(10000): # Don't infinite loop while learning
if args.use_joint_pol_val:
action = select_action_actor_critic(state)
else:
action = select_action(state)
reward = -math.log(
reward_net(
torch.cat((Variable(
torch.from_numpy(state).unsqueeze(0)).type(dtype),