def main(env_name, render=False):
env = gym.make(env_name)
# Inicialize seu agente aqui
agent = RandomAgent(env)
for episode_i in range(100000):
state = env.reset()
done = False
while not done:
if render and episode_i % 10 == 0:
env.render()
# Ação do seu agente aqui
action = agent.act(state)
state, reward, done, info = env.step(action)
import time
import gym
from agent import RandomAgent
env = gym.make("CartPole-v1")
agent = RandomAgent(env.action_space)
episode_count = 10
reward = 0
done = False
for i in range(episode_count):
ob = env.reset()
while True:
action = agent.act(ob, reward, done)
ob, reward, done, info = env.step(action)
if done:
print("Game Finished!")
break
env.render()
time.sleep(1 / 30)
env.close()
# set seed
random.seed(args.seed)
np.random.seed(args.seed)
env = MazeEnv(args, args.game_name, args.graph_param, args.game_len,
args.gamma)
# agent
if args.agent == 'random':
agent = RandomAgent(args, env)
NUM_GRAPH = 100
NUM_ITER = 32
ep_rews = []
for graph_id in range(NUM_GRAPH):
for _ in range(NUM_ITER):
ep_rew = 0
state, info = env.reset(graph_index=graph_id)
done = False
while not done:
action = agent.act(state)
state, rew, done, info = env.step(action)
ep_rew += rew
ep_rews.append(ep_rew)
string = 'Graph={:02d}/{:02d}, Return={:.4f}'
print(string.format(graph_id, NUM_GRAPH, sum(ep_rews) / len(ep_rews)))
print('Avg. Ep Return={:.4f}'.format(sum(ep_rews) / len(ep_rews)))
print('This should be around 0.0455')
def challenger_round():
challengers = []
leaders = []
leader_checkpoints = os.listdir(LEADER_DIR)
# Need to share the same schedule with all challengers, so they all anneal
# at same rate
epsilon_schedule = LinearSchedule(EPS_START, EPS_END, TRAIN_FRAMES)
for i in xrange(NUM_LEADERS):
challenger = try_gpu(
DQNAgent(6,
epsilon_schedule,
OBSERVATION_MODE,
lr=LR,
max_grad_norm=GRAD_CLIP_NORM))
if i < len(leader_checkpoints):
leader = try_gpu(
DQNAgent(6, LinearSchedule(0.1, 0.1, 500000),
OBSERVATION_MODE))
leader_path = os.path.join(LEADER_DIR, leader_checkpoints[i])
print "LOADING CHECKPOINT: {}".format(leader_path)
challenger.load_state_dict(
torch.load(leader_path,
map_location=lambda storage, loc: storage))
leader.load_state_dict(
torch.load(leader_path,
map_location=lambda storage, loc: storage))
else:
leader = RandomAgent(6)
print "INITIALIZING NEW CHALLENGER AND LEADER"
challengers.append(challenger)
leaders.append(leader)
if CHALLENGER_DIR is not None:
challengers = []
# Load in all of the leaders
for checkpoint in os.listdir(CHALLENGER_DIR):
path = os.path.join(CHALLENGER_DIR, checkpoint)
print "LOADING FROM CHALLENGER_DIR: {}".format(path)
challenger = try_gpu(
DQNAgent(6,
LinearSchedule(0.05, 0.05, 1),
CHALLENGER_OBSERVATION_MODE,
lr=LR,
max_grad_norm=GRAD_CLIP_NORM,
name=checkpoint))
challenger.load_state_dict(
torch.load(path, map_location=lambda storage, loc: storage))
challengers.append(challenger)
challenger = EnsembleDQNAgent(challengers)
leader = EnsembleDQNAgent(leaders)
if OPPONENT is not None or HUMAN:
leader = NoOpAgent()
replay_buffer = ReplayBuffer(1000000)
rewards = collections.deque(maxlen=1000)
frames = 0 # number of training frames seen
episodes = 0 # number of training episodes that have been played
with tqdm(total=TRAIN_FRAMES) as progress:
# Each loop completes a single episode
while frames < TRAIN_FRAMES:
states = env.reset()
challenger.reset()
leader.reset()
episode_reward = 0.
episode_frames = 0
# Each loop completes a single step, duplicates _evaluate() to
# update at the appropriate frame #s
for _ in xrange(MAX_EPISODE_LENGTH):
frames += 1
episode_frames += 1
action1 = challenger.act(states[0])
action2 = leader.act(states[1])
next_states, reward, done = env.step(action1, action2)
episode_reward += reward
# NOTE: state and next_state are LazyFrames and must be
# converted to np.arrays
replay_buffer.add(
Experience(states[0], action1._action_index, reward,
next_states[0], done))
states = next_states
if len(replay_buffer) > 50000 and \
frames % 4 == 0:
experiences = replay_buffer.sample(32)
challenger.update_from_experiences(experiences)
if frames % 10000 == 0:
challenger.sync_target()
if frames % SAVE_FREQ == 0:
# TODO: Don't access internals
for agent in challenger._agents:
path = os.path.join(LEADER_DIR,
agent.name + "-{}".format(frames))
print "SAVING CHECKPOINT TO: {}".format(path)
torch.save(agent.state_dict(), path)
#path = os.path.join(
# LEADER_DIR, challenger.name + "-{}".format(frames))
#torch.save(challenger.state_dict(), path)
if frames >= TRAIN_FRAMES:
break
if done:
break
if episodes % 300 == 0:
print "Evaluation: {}".format(
evaluate(challenger, leader, EPISODES_EVALUATE_TRAIN))
print "Episode reward: {}".format(episode_reward)
episodes += 1
rewards.append(episode_reward)
stats = challenger.stats
stats["Avg Episode Reward"] = float(sum(rewards)) / len(rewards)
stats["Num Episodes"] = episodes
stats["Replay Buffer Size"] = len(replay_buffer)
progress.set_postfix(stats, refresh=False)
progress.update(episode_frames)
episode_frames = 0
possible_actions = [0, 1] # Cooperate or Defect
cooperator, defector = RandomAgent(possible_actions, p=0.9), RandomAgent(possible_actions, p=0.1)
# Stateless interactions (agents do not have memory)
s = None
n_iter = 1000
for i in range(n_iter):
# A full episode:
done = False
while not done:
# Agents decide
a0 = cooperator.act()
a1 = defector.act()
# World changes
new_s, (r0, r1), done, _ = env.step(([a0], [a1]))
# Agents learn
cooperator.update(s, (a0, a1), (r0, r1), new_s )
defector.update(s, (a1, a0), (r1, r0), new_s )
s = new_s
print(r0, r1)
env.reset()