def get_agent(name, *args, **kwargs):
if name == 'QLearningAgent':
import qlearning
return qlearning.QLearningAgent(*args, **kwargs)
elif name == 'DQNAgent':
import dqn
return dqn.DQNAgent(*args, **kwargs)
else:
raise Exception('Invalid agent name.')
def make_agent(args, input_shape, num_actions: int, output_dir: str):
replay_memory = ReplayMemory(max_size=args.replay_buffer_size,
batch_size=args.batch_size)
exploration_strategy = EpsilonGreedyStrategy(
epsilon_max=args.epsilon_max,
epsilon_min=args.epsilon_min,
epsilon_decay=args.epsilon_decay,
)
hyper_parameters = dqn.HyperParameters(args.learning_rate, args.gamma)
if args.dueling:
dqn_class = dqn.make_dqn_dueling
else:
dqn_class = dqn.make_dqn
if args.double:
agent = dqn.DoubleDQNAgent(
target_dqn=dqn_class(
input_shape=input_shape,
hidden_dim=args.hidden_dim,
num_actions=num_actions,
),
target_update_rate=args.target_update_rate,
dqn=dqn_class(
input_shape=input_shape,
hidden_dim=args.hidden_dim,
num_actions=num_actions,
),
replay_memory=replay_memory,
exploration_strategy=exploration_strategy,
hyper_parameters=hyper_parameters,
num_actions=num_actions,
output_dir=output_dir,
)
else:
agent = dqn.DQNAgent(
dqn=dqn_class(
input_shape=input_shape,
hidden_dim=args.hidden_dim,
num_actions=num_actions,
),
replay_memory=replay_memory,
exploration_strategy=exploration_strategy,
hyper_parameters=hyper_parameters,
num_actions=num_actions,
output_dir=output_dir,
)
return agent
def main():
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
learning_rate = 1e-3
model = build_model(state_size, action_size, learning_rate)
agent = dqn.DQNAgent(model, state_size, action_size)
agent.fit()
env = gym.wrappers.Monitor(env,
"./video",
video_callable=lambda episode_id: True,
force=True)
for _ in range(10):
agent.play(env)
env = gym.make('CartPole-v0')
exprep = exp_replay.ExpReplay(mem_size=MEM_SIZE,
start_mem=START_MEM,
state_size=STATE_SIZE,
kth=-1,
batch_size=BATCH_SIZE)
sess = tf.Session()
with tf.device('/{}:0'.format(DEVICE)):
agent = dqn.DQNAgent(session=sess,
epsilon=EPSILON,
epsilon_anneal=EPSILON_DECAY,
end_epsilon=END_EPSILON,
lr=LEARNING_RATE,
gamma=DISCOUNT_FACTOR,
state_size=4,
action_size=len(ACTIONS),
n_hidden_1=10,
n_hidden_2=10)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
if os.path.isdir(MODEL_DIR):
saver.restore(sess, MODEL_PATH)
agent.epsilon = agent.end_epsilon
print 'restored model'
if TRAIN:
exprep = pickle.load(open(MEMORY_PATH, "rb"))
history = [e_length for e_length in train(agent, exprep, env)]
saver.save(sess, MODEL_PATH)
def __init__(self, state_size, action_size):
self.agent = D.DQNAgent(state_size, action_size)
self.N = state_size
self.reward = []
q_net.cuda()
agent = dqn.DistributionalDQNAgent(q_net, args.double_dqn,
train_env.num_actions,
args.num_atoms, -10, 10)
else:
if args.dueling:
q_net_builder = model.build_dueling_network
else:
q_net_builder = model.build_basic_network
q_net = q_net_builder(args.num_frames, args.frame_size,
train_env.num_actions, args.noisy_net,
args.sigma0, args.net)
q_net.cuda()
agent = dqn.DQNAgent(q_net, args.double_dqn, train_env.num_actions)
if args.noisy_net:
train_policy = GreedyEpsilonPolicy(0, agent)
else:
train_policy = LinearDecayGreedyEpsilonPolicy(args.train_start_eps,
args.train_final_eps,
args.train_eps_num_steps,
agent)
eval_policy = GreedyEpsilonPolicy(args.eval_eps, agent)
replay_memory = ReplayMemory(args.replay_buffer_size)
replay_memory.burn_in(train_env, agent, args.burn_in_frames)
evaluator = lambda logger: evaluate(eval_env, eval_policy, 10, logger)
train(agent, train_env, train_policy, replay_memory, args.gamma,
agent.epsilon_decay()
print("当前的epsilon为{}".format(agent.epsilon))
agent.learn(buffer=buffer, num_steps=128, batch_size=256)
if i % 20 == 0 and i > 0:
saver.save(sess, save_path)
print("save model successfully!")
if __name__ == "__main__":
env = SnakeEnv(gameSpeed=5, train_model=True)
save_path = "./snake/model"
# ob = env.reset()
# print(ob,type(ob),ob.shape)
buffer = ReplayBuffer(buffer_size=8192)
sess = tf.Session()
agent = dqn.DQNAgent(sess=sess,
epsilon=0.9,
epsilon_anneal=0.01,
end_epsilon=0.1,
lr=0.001,
gamma=0.9,
state_size=3,
action_size=4,
name_scope="dqn")
sess.run(tf.global_variables_initializer())
train(agent=agent,
buffer=buffer,
env=env,
num_episodes=10000,
max_steps=100,
save_path=save_path)
import dqn as dqn
import gym
import numpy as np
episodes = 500
EPISODES = 100
batch_size =32
TRAIN_MODE = True
TEST_MODE = False
if __name__ == "__main__":
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
print(state_size,action_size)
agent = dqn.DQNAgent(state_size,action_size)
if TRAIN_MODE:
#Iterate the game
for e in range(EPISODES):
#rest state in the beginning of each game
state = env.reset()
state = np.reshape(state, [1,4])
#time_t represents each frame of the game
for time_t in range(500):
# turn this on if you want to render
# env.render()
# Decide action
action = agent.act(state)
# Advance the game to the next frame based on the action.