def test():
# trained model directory
directory = "./preTrained"
filename = "ddpg"
# initialize DDPG agent
agent = DDPG(
state_dim=env.observation_space.shape[0] -
1, # state_dim=env.observation_space.shape[0]
action_dim=env.action_space.shape[0],
action_bounds=env.action_space.high[0],
lr=0)
# load trained agent
assert os.path.exists(
directory), "Trained model not exists, try running train.py first."
agent.load(directory, filename)
for epoch in range(max_episode):
# reset environment
state = env.reset()
done = False
rewards = 0
while not done:
action = agent.select_action(state)
# perform one step update on pendulum
next_state, reward, done, _ = env.step(action)
# go to next state
state = next_state
rewards += reward
# render envrionment
env.render()
print("Episode:{:2d}, Rewards:{:3f}".format(epoch, rewards))
def train():
# save trained model under preTrained directory
directory = "./preTrained"
filename = "ddpg"
# set epsilon exploration rate and decay rate
epsilon = 0.2
eps_min = 1e-3
eps_decay = 2e-3
gaussian_exploration_noise = 0.2
# set learning rate and batch size
lr = 1e-3
batch_size = 128
# initialize replay memory
replay_buffer = ReplayBuffer(max_size=5e4)
# rewards for each episode / for plot
rewards = np.zeros(max_episode)
# initialize DDPG agent
agent = DDPG(
state_dim=env.observation_space.shape[0] -
1, # state_dim=env.observation_space.shape[0]
action_dim=env.action_space.shape[0],
action_bounds=env.action_space.high[0],
lr=lr)
for epoch in range(max_episode):
# reset environment
state = env.reset()
done = False
# epsilon decay
epsilon = eps_min if (epsilon - eps_decay) < 0 else (epsilon -
eps_decay)
while not done:
if np.random.random_sample() > epsilon:
action = agent.select_action(state)
action = action + np.random.normal(0,
gaussian_exploration_noise)
else:
action = np.array(
np.random.uniform(env.action_space.low[0],
env.action_space.high[0])).reshape(1, )
# perform one step update on pendulum
next_state, reward, done, _ = env.step(action)
env.render()
replay_buffer.add((state, action, reward, next_state, done))
# go to next state
state = next_state
# store rewards
rewards[epoch] += reward
# update the DDPG agent sampled on replay buffer and n_iter times
agent.update(buffer=replay_buffer,
n_iter=10,
batch_size=batch_size)
if rewards[epoch] > -1.0:
print("task solved!\n")
# save trained agent
if not os.path.exists(directory):
os.mkdir(directory)
agent.save(directory, filename)
# print rewards of current episode
if epoch % 10 == 0:
print('train epoch:', epoch, 'rewards:', rewards[epoch])
return rewards