def which_env(name):
if 'Grid' in name:
env = ImgObsWrapper(gym.make(name))
test_env = ImgObsWrapper(gym.make(name))
else:
env = make_atari(name)
test_env = make_atari(name)
return env, test_env, (env.observation_space.shape, env.action_space.n)
def inner_objective(
ind: cgp.IndividualSingleGenome,
network_params: dict,
curriculum_params: dict,
seeds
) -> float:
rule = ind.to_torch()
reward_per_seed = []
reward_per_seed_mean = []
for seed in seeds:
seed = int(seed)
torch.manual_seed(seed=seed)
rng = np.random.default_rng(seed=seed)
# environment and network initialization
env = DynamicMiniGrid(seed=seed)
env = ImgObsWrapper(env)
state = env.respawn()["image"][:,:,0].flatten()
policy_net = Network(n_inputs=np.size(state), **network_params)
rewards_over_alterations = run_curriculum(env=env, net=policy_net, rule=rule, **curriculum_params, rng=rng)
reward_per_seed.append(rewards_over_alterations)
reward_per_seed_mean.append(np.mean(rewards_over_alterations))
ind.reward_matrix = reward_per_seed
reward_mean = np.mean(reward_per_seed_mean)
return float(reward_mean)
def __init__(self,
env_id,
is_render,
env_idx,
child_conn,
history_size=1,
h=84,
w=84,
life_done=True,
sticky_action=False,
p=0.25):
super(GridEnvironment, self).__init__()
self.daemon = True
self.env = ImgObsWrapper(
RGBImgObsWrapper(ReseedWrapper(gym.make(env_id))))
self.env_id = env_id
self.is_render = is_render
self.env_idx = env_idx
self.steps = 0
self.episode = 0
self.rall = 0
self.recent_rlist = deque(maxlen=100)
self.child_conn = child_conn
self.sticky_action = sticky_action
self.last_action = 0
self.p = p
self.history_size = history_size
self.history = np.zeros([history_size, h, w])
self.h = h
self.w = w
self.reset()
def init_env(self):
env = ImgObsWrapper(self.init())
env.reset()
print("agent pos: {}".format(env.agent_pos))
self.action_space = env.action_space
self.action_dim = env.action_space.n
self.obs_dim = env.observation_space.shape
return env
def get_env(env_name):
env = gym.make(env_name)
if env_name.startswith('MiniGrid'):
env = ImgObsWrapper(env)
# TODO include atari here or put get_env in gym_utils
# env = make_atari('SpaceInvadersNoFrameskip-v0')
# env = WrapFrame(env)
return env
def thunk():
env = gym.make(gym_id)
env = ImgObsWrapper(env)
env = gym.wrappers.RecordEpisodeStatistics(env)
if args.capture_video:
if idx == 0:
env = Monitor(env, f'videos/{experiment_name}')
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
def mini_grid_wrapper(env_id, max_frames=0, clip_rewards=True):
env = gym.make(env_id)
env = ReseedWrapper(env, seeds=[0])
env = RGBImgObsWrapper(env)
env = ImgObsWrapper(env)
if max_frames:
env = pfrl.wrappers.ContinuingTimeLimit(
env, max_episode_steps=max_frames)
# env = atari_wrappers.MaxAndSkipEnv(env, skip=0)
env = atari_wrappers.wrap_deepmind(
env, episode_life=False, clip_rewards=clip_rewards)
return env
def main(argv):
env = e_lib.EmptyMultigoal(size=FLAGS.size,
n_goals=FLAGS.n_goals,
n_traps=FLAGS.n_traps)
env = e_lib.SymbolicObsWrapper(env)
env = ImgObsWrapper(env)
env = helx.rl.environment.from_gym(env)
n_features = jnp.prod(env.observation_spec().shape)
logger = helx.logging.TerminalLogger()
agent = a_lib.SarsaLambda(env, FLAGS.alpha, FLAGS.lamda, n_features,
logger)
helx.rl.experiment.run(env, agent, FLAGS.train_episodes)
helx.rl.experiment.run(env, agent, FLAGS.eval_episodes, True)
def __init__(self, name, horizon=None, gamma=0.99, history_length=4,
fixed_seed=None, use_pixels=False):
"""
Constructor.
Args:
name (str): name of the environment;
horizon (int, None): the horizon;
gamma (float, 0.99): the discount factor;
history_length (int, 4): number of frames to form a state;
fixed_seed (int, None): if passed, it fixes the seed of the
environment at every reset. This way, the environment is fixed
rather than procedurally generated;
use_pixels (bool, False): if True, MiniGrid's default 7x7x3
observations is converted to an image of resolution 56x56x3.
"""
# MDP creation
self._not_pybullet = True
self._first = True
env = gym.make(name)
obs_high = 10.
if use_pixels:
env = RGBImgPartialObsWrapper(env) # Get pixel observations
obs_high = 255.
env = ImgObsWrapper(env) # Get rid of the 'mission' field
self.env = env
self._fixed_seed = fixed_seed
self._img_size = env.observation_space.shape[0:2]
self._history_length = history_length
# Get the default horizon
if horizon is None:
horizon = self.env.max_steps
# MDP properties
action_space = Discrete(self.env.action_space.n)
observation_space = Box(
low=0., high=obs_high, shape=(history_length, self._img_size[1], self._img_size[0]))
self.env.max_steps = horizon + 1 # Hack to ignore gym time limit (do not use np.inf, since MiniGrid returns r(t) = 1 - 0.9t/T)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
Environment.__init__(self, mdp_info)
self._state = None
def calculate_validation_fitness(champion, seed, network_params, curriculum_params):
rule = champion.to_torch()
torch.manual_seed(seed=seed)
rng = np.random.default_rng(seed=seed)
# environment and network initialization
env = DynamicMiniGrid(seed=seed)
env = ImgObsWrapper(env)
state = env.respawn()["image"][:, :, 0].flatten()
policy_net = Network(n_inputs=np.size(state), **network_params)
rewards_over_alterations = run_curriculum(env=env, net=policy_net, rule=rule, **curriculum_params, rng=rng)
return rewards_over_alterations
def get_env_constructor(self, env_name):
env_type = self.get_env_type(env_name)
if env_type == 'mg':
constructor = lambda: MiniGridRewardNormalize(
ImgObsWrapper(gym.make(env_name)),
scale=self.env_infos[env_name].reward_scale,
shift=self.env_infos[env_name].reward_shift)
elif env_type == 'gym':
constructor = lambda: GymRewardNormalize(
gym.make(env_name),
scale=self.env_infos[env_name].reward_scale,
shift=self.env_infos[env_name].reward_shift)
elif env_type in ['tab', 'vcomp']:
constructor = self.env_infos[env_name].constructor
else:
assert False
return constructor
def set_env(params):
if params.env == 'hanoi':
from hanoi_env.env import HanoiEnv
params.model_type = 'rnn'
env = HanoiEnv()
env.set_env_parameters(max_count=params.max_count,
num_disks=params.num_disks,
num_pegs=params.num_pegs,
allow_impossible=params.allow_impossible,
continual=params.continual,
initial_peg=params.initial_peg)
elif params.env == 'lightbot_minigrid':
from gym_minigrid.envs import LightbotEnv as LightbotMinigridEnv
from gym_minigrid.wrappers import ImgObsWrapper, AgentViewWrapper
params.model_type = 'cnn'
env = LightbotMinigridEnv(params.puzzle_name,
reward_fn=params.rewards,
max_steps=params.max_count,
toggle_ontop=False)
env = ImgObsWrapper(AgentViewWrapper(env, agent_view_size=9))
elif params.env == 'lightbot':
from lightbot_env.env import LightbotEnv
params.model_type = 'mlp'
env = LightbotEnv(params.puzzle_name)
env.set_env_parameters(max_count=params.max_count,
testing=params.testing,
reward_fn=params.rewards,
random_init=params.random_init,
allow_impossible=params.allow_impossible)
elif params.env == 'fourrooms':
from fourrooms.fourrooms import Fourrooms
params.model_type = 'mlp'
env = Fourrooms(max_count=params.max_count)
elif params.env == 'fourrooms_minigrid':
params.model_type = 'cnn'
raise NotImplementedError
return env, params
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
gin.parse_config_files_and_bindings(
[os.path.join(mon_minigrid.GIN_FILES_PREFIX, 'classic_fourrooms.gin')],
bindings=FLAGS.gin_bindings,
skip_unknown=False)
env_id = mon_minigrid.register_environment()
env = gym.make(env_id)
env = RGBImgObsWrapper(env) # Get pixel observations
env = ImgObsWrapper(env) # Get rid of the 'mission' field
env.reset()
num_frames = 0
max_num_frames = 500
if not tf.io.gfile.exists(FLAGS.file_path):
tf.io.gfile.makedirs(FLAGS.file_path)
undisc_return = 0
while num_frames < max_num_frames:
# Act randomly
obs, reward, done, _ = env.step(env.action_space.sample())
undisc_return += reward
num_frames += 1
# Draw environment frame just for simple visualization
plt.imshow(obs)
path = os.path.join(FLAGS.file_path, 'obs_{}.png'.format(num_frames))
plt.savefig(path)
plt.clf()
if done:
break
print('Undiscounted return: %.2f' % undisc_return)
env.close()
def get_env_constructor(self, env_name):
env_type = self.get_env_type(env_name)
if env_type == 'mg':
constructor = lambda: MiniGridRewardNormalize(
ImgObsWrapper(gym.make(env_name)),
scale=self.env_infos[env_name].reward_scale,
shift=self.env_infos[env_name].reward_shift)
elif env_type == 'gym':
constructor = lambda: GymRewardNormalize(
gym.make(env_name),
scale=self.env_infos[env_name].reward_scale,
shift=self.env_infos[env_name].reward_shift)
elif env_type == 'tab':
"""
Here you should explicitly design the reward structure
"""
constructor = self.env_infos[env_name].constructor
else:
assert False
return constructor
self.grid.wall_rect(0, 0, width, height)
# Place the goals
for _ in range(self.n_goals):
self.place_obj(Goal())
# Place the traps
for _ in range(self.n_traps):
self.place_obj(Lava())
# Place the agent
if self.agent_start_pos is not None:
self.agent_pos = self.agent_start_pos
self.agent_dir = self.agent_start_dir
else:
self.place_agent()
self.mission = "get to the green goal square, avoid the lava"
if __name__ == "__main__":
# debugging
env = EmptyMultigoal(size=5, n_goals=1, n_traps=1)
env = SymbolicObsWrapper(env)
env = PartialObsWrapper(env, agent_view_size=1)
env = ImgObsWrapper(env)
o = env.reset()
print(o.shape)
env.render()
print(o)
def main(args):
env = gym.make(args.env)
if 'MiniGrid' in args.env:
env = ImgObsWrapper(env)
path = args.base_path + args.env
os.makedirs(path, exist_ok=True)
# obs_shape = np.prod(env.observation_space.shape).astype(int)
obs_shape = env.observation_space.shape
act_shape = env.action_space.n
q = QNetwork(obs_shape, act_shape)
q_target = QNetwork(obs_shape, act_shape)
opt = optim.Adam(lr=args.lr, params=q.parameters())
memory = Memory(capacity=args.memory)
scheduler = LinearSchedule(schedule_timesteps=int(args.max_steps * 0.1), final_p=0.01)
avg_rw = deque(maxlen=40)
avg_len = deque(maxlen=40)
def get_action(s, t):
s = torch.Tensor(s[None,:])
_q = q(s)
if np.random.sample() > scheduler.value:
best_action = np.argmax(_q.detach(), axis=-1).item()
else:
best_action = np.random.randint(0, act_shape)
scheduler.update(t)
return best_action
def train(batch):
batch = Transition(*zip(*batch))
s = torch.Tensor(batch.state)
a = torch.Tensor(one_hot(np.array(batch.action), num_classes=act_shape))
r = torch.Tensor(batch.reward)
d = torch.Tensor(batch.done)
s1 = torch.Tensor(batch.next_state)
value = (q(s) * a).sum(dim=-1)
next_value = r + args.gamma * (1. - d) * torch.max(q_target(s1), dim=-1)[0]
loss = (.5 * (next_value - value) ** 2).mean()
opt.zero_grad()
loss.backward()
opt.step()
state = env.reset()
q_target.load_state_dict(q.state_dict())
ep_rw = 0
ep_len = 0
ep = 0
for t in range(args.max_steps):
action = get_action(state, t)
next_state, reward, done, _ = env.step(action)
memory.push(state, action, next_state, reward, done)
ep_rw += reward
ep_len += 1
state = next_state.copy()
if done:
ep += 1
avg_rw.append(ep_rw)
avg_len.append(ep_len)
ep_rw = 0
ep_len = 0
state = env.reset()
if t % args.train_every == 0 and len(memory) > args.batch_size:
batch = memory.sample(batch_size=args.batch_size)
train(batch)
if t % args.update_every == 0:
q_target.load_state_dict(q.state_dict())
print(f't:{t}\tep:{ep}\tavg_rw:{np.mean(avg_rw)}\tavg_len:{np.mean(avg_len)}\teps:{scheduler.value}')
env = Monitor(env, directory=path)
for ep in range(4):
s = env.reset()
while True:
a = get_action(s, t=0)
s1, r, d, _ = env.step(a)
s = s1.copy()
if d:
break
def main(run_id=0, checkpoint=None, rec_interval=10, save_interval=100):
print({section: dict(config[section]) for section in config.sections()})
train_method = grid_config['TrainMethod']
# Create environment
env_id = grid_config['EnvID']
env_type = grid_config['EnvType']
if env_type == 'mario':
print('Mario environment not fully implemented - thomaseh')
raise NotImplementedError
env = BinarySpaceToDiscreteSpaceEnv(
gym_super_mario_bros.make(env_id), COMPLEX_MOVEMENT)
elif env_type == 'atari':
env = gym.make(env_id)
elif env_type == 'grid':
env = ImgObsWrapper(RGBImgObsWrapper(gym.make(env_id)))
else:
raise NotImplementedError
input_size = env.observation_space.shape # 4
output_size = env.action_space.n # 2
if 'Breakout' in env_id:
output_size -= 1
env.close()
# Load configuration parameters
is_load_model = checkpoint is not None
is_render = False
model_path = 'models/{}_{}_run{}_model'.format(env_id, train_method, run_id)
predictor_path = 'models/{}_{}_run{}_vae'.format(env_id, train_method, run_id)
writer = SummaryWriter(logdir='runs/{}_{}_run{}'.format(env_id, train_method, run_id))
use_cuda = grid_config.getboolean('UseGPU')
use_gae = grid_config.getboolean('UseGAE')
use_noisy_net = grid_config.getboolean('UseNoisyNet')
lam = float(grid_config['Lambda'])
num_worker = int(grid_config['NumEnv'])
num_step = int(grid_config['NumStep'])
num_rollouts = int(grid_config['NumRollouts'])
num_pretrain_rollouts = int(grid_config['NumPretrainRollouts'])
ppo_eps = float(grid_config['PPOEps'])
epoch = int(grid_config['Epoch'])
mini_batch = int(grid_config['MiniBatch'])
batch_size = int(num_step * num_worker / mini_batch)
learning_rate = float(grid_config['LearningRate'])
entropy_coef = float(grid_config['Entropy'])
gamma = float(grid_config['Gamma'])
int_gamma = float(grid_config['IntGamma'])
clip_grad_norm = float(grid_config['ClipGradNorm'])
ext_coef = float(grid_config['ExtCoef'])
int_coef = float(grid_config['IntCoef'])
sticky_action = grid_config.getboolean('StickyAction')
action_prob = float(grid_config['ActionProb'])
life_done = grid_config.getboolean('LifeDone')
reward_rms = RunningMeanStd()
obs_rms = RunningMeanStd(shape=(1, 1, 84, 84))
pre_obs_norm_step = int(grid_config['ObsNormStep'])
discounted_reward = RewardForwardFilter(int_gamma)
hidden_dim = int(grid_config['HiddenDim'])
if train_method == 'RND':
agent = RNDAgent
elif train_method == 'generative':
agent = GenerativeAgent
else:
raise NotImplementedError
if grid_config['EnvType'] == 'atari':
env_type = AtariEnvironment
elif grid_config['EnvType'] == 'mario':
env_type = MarioEnvironment
elif grid_config['EnvType'] == 'grid':
env_type = GridEnvironment
else:
raise NotImplementedError
# Initialize agent
agent = agent(
input_size,
output_size,
num_worker,
num_step,
gamma,
history_size=1,
lam=lam,
learning_rate=learning_rate,
ent_coef=entropy_coef,
clip_grad_norm=clip_grad_norm,
epoch=epoch,
batch_size=batch_size,
ppo_eps=ppo_eps,
use_cuda=use_cuda,
use_gae=use_gae,
use_noisy_net=use_noisy_net,
update_proportion=1.0,
hidden_dim=hidden_dim
)
# Load pre-existing model
if is_load_model:
print('load model...')
if use_cuda:
agent.model.load_state_dict(torch.load(model_path))
agent.vae.load_state_dict(torch.load(predictor_path))
else:
agent.model.load_state_dict(
torch.load(model_path, map_location='cpu'))
agent.vae.load_state_dict(torch.load(predictor_path, map_location='cpu'))
print('load finished!')
# Create workers to run in environments
works = []
parent_conns = []
child_conns = []
for idx in range(num_worker):
parent_conn, child_conn = Pipe()
work = env_type(
env_id, is_render, idx, child_conn, sticky_action=sticky_action,
p=action_prob, life_done=life_done,
)
work.start()
works.append(work)
parent_conns.append(parent_conn)
child_conns.append(child_conn)
states = np.zeros([num_worker, 1, 84, 84], dtype='float32')
sample_episode = 0
sample_rall = 0
sample_step = 0
sample_env_idx = 0
sample_i_rall = 0
global_update = 0
global_step = 0
# Initialize stats dict
stats = {
'total_reward': [],
'ep_length': [],
'num_updates': [],
'frames_seen': [],
}
# Main training loop
while True:
total_state = np.zeros([num_worker * num_step, 1, 84, 84], dtype='float32')
total_next_obs = np.zeros([num_worker * num_step, 1, 84, 84])
total_reward, total_done, total_next_state, total_action, \
total_int_reward, total_ext_values, total_int_values, total_policy, \
total_policy_np = [], [], [], [], [], [], [], [], []
# Step 1. n-step rollout (collect data)
for step in range(num_step):
actions, value_ext, value_int, policy = agent.get_action(states / 255.)
for parent_conn, action in zip(parent_conns, actions):
parent_conn.send(action)
next_obs = np.zeros([num_worker, 1, 84, 84])
next_states = np.zeros([num_worker, 1, 84, 84])
rewards, dones, real_dones, log_rewards = [], [], [], []
for idx, parent_conn in enumerate(parent_conns):
s, r, d, rd, lr, stat = parent_conn.recv()
next_states[idx] = s
rewards.append(r)
dones.append(d)
real_dones.append(rd)
log_rewards.append(lr)
next_obs[idx, 0] = s[0, :, :]
total_next_obs[idx * num_step + step, 0] = s[0, :, :]
if rd:
stats['total_reward'].append(stat[0])
stats['ep_length'].append(stat[1])
stats['num_updates'].append(global_update)
stats['frames_seen'].append(global_step)
rewards = np.hstack(rewards)
dones = np.hstack(dones)
real_dones = np.hstack(real_dones)
# Compute total reward = intrinsic reward + external reward
intrinsic_reward = agent.compute_intrinsic_reward(next_obs / 255.)
intrinsic_reward = np.hstack(intrinsic_reward)
sample_i_rall += intrinsic_reward[sample_env_idx]
for idx, state in enumerate(states):
total_state[idx * num_step + step] = state
total_int_reward.append(intrinsic_reward)
total_reward.append(rewards)
total_done.append(dones)
total_action.append(actions)
total_ext_values.append(value_ext)
total_int_values.append(value_int)
total_policy.append(policy)
total_policy_np.append(policy.cpu().numpy())
states = next_states[:, :, :, :]
sample_rall += log_rewards[sample_env_idx]
sample_step += 1
if real_dones[sample_env_idx]:
sample_episode += 1
writer.add_scalar('data/reward_per_epi', sample_rall, sample_episode)
writer.add_scalar('data/reward_per_rollout', sample_rall, global_update)
writer.add_scalar('data/step', sample_step, sample_episode)
sample_rall = 0
sample_step = 0
sample_i_rall = 0
# calculate last next value
_, value_ext, value_int, _ = agent.get_action(np.float32(states) / 255.)
total_ext_values.append(value_ext)
total_int_values.append(value_int)
# --------------------------------------------------
total_reward = np.stack(total_reward).transpose().clip(-1, 1)
total_action = np.stack(total_action).transpose().reshape([-1])
total_done = np.stack(total_done).transpose()
total_ext_values = np.stack(total_ext_values).transpose()
total_int_values = np.stack(total_int_values).transpose()
total_logging_policy = np.vstack(total_policy_np)
# Step 2. calculate intrinsic reward
# running mean intrinsic reward
total_int_reward = np.stack(total_int_reward).transpose()
total_reward_per_env = np.array([discounted_reward.update(reward_per_step) for reward_per_step in
total_int_reward.T])
mean, std, count = np.mean(total_reward_per_env), np.std(total_reward_per_env), len(total_reward_per_env)
reward_rms.update_from_moments(mean, std ** 2, count)
writer.add_scalar('data/raw_int_reward_per_epi', np.sum(total_int_reward) / num_worker, sample_episode)
writer.add_scalar('data/raw_int_reward_per_rollout', np.sum(total_int_reward) / num_worker, global_update)
# normalize intrinsic reward
total_int_reward /= np.sqrt(reward_rms.var)
writer.add_scalar('data/int_reward_per_epi', np.sum(total_int_reward) / num_worker, sample_episode)
writer.add_scalar('data/int_reward_per_rollout', np.sum(total_int_reward) / num_worker, global_update)
# -------------------------------------------------------------------------------------------
# logging Max action probability
writer.add_scalar('data/max_prob', softmax(total_logging_policy).max(1).mean(), sample_episode)
# Step 3. make target and advantage
# extrinsic reward calculate
ext_target, ext_adv = make_train_data(total_reward,
total_done,
total_ext_values,
gamma,
num_step,
num_worker)
# intrinsic reward calculate
# None Episodic
int_target, int_adv = make_train_data(total_int_reward,
np.zeros_like(total_int_reward),
total_int_values,
int_gamma,
num_step,
num_worker)
# add ext adv and int adv
total_adv = int_adv * int_coef + ext_adv * ext_coef
# -----------------------------------------------
# Step 4. update obs normalize param
# obs_rms.update(total_next_obs)
# -----------------------------------------------
# Step 5. Training!
# random_obs_choice = np.random.randint(total_next_obs.shape[0])
# random_obs = total_next_obs[random_obs_choice].copy()
total_next_obs /= 255.
if global_update < num_pretrain_rollouts:
recon_losses, kld_losses = agent.train_just_vae(total_state / 255., total_next_obs)
else:
recon_losses, kld_losses = agent.train_model(total_state / 255., ext_target, int_target, total_action,
total_adv, total_next_obs, total_policy)
writer.add_scalar('data/reconstruction_loss_per_rollout', np.mean(recon_losses), global_update)
writer.add_scalar('data/kld_loss_per_rollout', np.mean(kld_losses), global_update)
global_step += (num_worker * num_step)
if global_update % rec_interval == 0:
with torch.no_grad():
# random_obs_norm = total_next_obs[random_obs_choice]
# reconstructed_state = agent.reconstruct(random_obs_norm)
# random_obs_norm = (random_obs_norm - random_obs_norm.min()) / (random_obs_norm.max() - random_obs_norm.min())
# reconstructed_state = (reconstructed_state - reconstructed_state.min()) / (reconstructed_state.max() - reconstructed_state.min())
# writer.add_image('Original', random_obs, global_update)
# writer.add_image('Original Normalized', random_obs_norm, global_update)
random_state = total_next_obs[np.random.randint(total_next_obs.shape[0])]
reconstructed_state = agent.reconstruct(random_state)
writer.add_image('Original', random_state, global_update)
writer.add_image('Reconstructed', reconstructed_state, global_update)
if global_update % save_interval == 0:
print('Saving model at global step={}, num rollouts={}.'.format(
global_step, global_update))
torch.save(agent.model.state_dict(), model_path + "_{}.pt".format(global_update))
torch.save(agent.vae.state_dict(), predictor_path + '_{}.pt'.format(global_update))
# Save stats to pickle file
with open('models/{}_{}_run{}_stats_{}.pkl'.format(env_id, train_method, run_id, global_update),'wb') as f:
pickle.dump(stats, f)
global_update += 1
if global_update == num_rollouts + num_pretrain_rollouts:
print('Finished Training.')
break
if reward_mean > 500:
break
def play(self, num_episodes, render=True):
"""Test the trained agent.
"""
for episode in range(num_episodes):
state = self.env.reset()
total_reward = 0.0
while True:
if render:
self.env.render()
action = self.get_action(state)
state, reward, done, _ = self.env.step(action)
total_reward += reward
if done:
print(
f"Total reward: {total_reward} in episode {episode + 1}"
)
break
if __name__ == "__main__":
env = gym.make("MiniGrid-Empty-8x8-v0")
env = RGBImgPartialObsWrapper(env) # Get pixel observations
env = ImgObsWrapper(env) # Get rid of the 'mission' field
agent = Agent(env)
print("Number of actions: ", agent.actions)
agent.train(percentile=99.9, num_iterations=64, num_episodes=128)
agent.play(num_episodes=3)
def BobEnv(size):
return ImgObsWrapper(RGBImgPartialObsWrapper(_BobEnv(size)))
def wrap_env(env, opt):
env = ImgObsWrapper(env)
env = FrameStack(env, k=opt.er.hist_len)
env = TorchWrapper(env, device=opt.device)
env = SeedWrapper(env, opt.seed) if opt.seed is not None else env
return env
def callback(_locals, _globals):
n_steps = _locals['_']
if n_steps and (n_steps % 1000 == 0):
print(n_steps)
print(_locals['episode_successes'])
# env.render()
# time.sleep(0.2)
n_steps += 1
# Returning False will stop training early
return True
# Create log dir
log_dir = f"{EXPERIMENT_DIR}/sb/gym"
os.makedirs(log_dir, exist_ok=True)
# Create environment
env_name = 'MiniGrid-FourRooms-v1'
env = FullyObsWrapper(ImgObsWrapper(gym.make(env_name)))
env.max_steps = 100000
# env.step = partial(stochastic_step, env)
env = DummyVecEnv([lambda: env])
# Train a model
model = DQN(policy=MlpPolicy,
env=env,
tensorboard_log=f"{EXPERIMENT_DIR}/sb/tensorboard/{env_name}")
model.learn(total_timesteps=10000000, callback=callback)
'max_timesteps': 500,
'action_mode': 'discrete'}
# create env
ENV = gym.make('BallBeamThrow-v0', **kwargs)
BD_BOUNDS = [[0, 3]]
INITIAL_GENOTYPE_SIZE = 11
MINI = False
EVALUATE_INDIVIDUAL = evaluate_beam
BD_GENOTYPE = 1
if ENV_NAME == 'grid':
import gym_minigrid # must still be imported
from gym_minigrid.wrappers import ImgObsWrapper # must still be imported
# create env
ENV = ImgObsWrapper(gym.make('MiniGrid-Empty-8x8-v0'))
BD_BOUNDS = [[0, 7], [0, 7]]
NB_CELLS = 64
INITIAL_GENOTYPE_SIZE = 11
MINI = False
EVALUATE_INDIVIDUAL = evaluate_grid
BD_GENOTYPE = 1
if ENV_NAME == 'bipedal':
# global variable for the environment
ENV = gym.make('BipedalWalker-v3')
BD_BOUNDS = [[-1, 1], [0, 1]]
INITIAL_GENOTYPE_SIZE = 118
MINI = False
EVALUATE_INDIVIDUAL = evaluate_bipedal
BD_GENOTYPE = 1
def _wrap_minigrid_env(env):
from gym_minigrid.wrappers import ImgObsWrapper
env = ImgObsWrapper(env) # Get rid of the 'mission' field
env = bench.Monitor(env, logger.get_dir())
return env