def train(cfg_name, resume):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'running on {device}')
cfg = load_cfg(cfg_name)
log = Logger(device=device)
envs = make_vec_envs(**cfg['env'])
model, n_start = init_model(cfg, envs, device, resume)
runner = EnvRunner(rollout_size=cfg['train']['rollout_size'],
envs=envs,
model=model,
device=device)
optim = ParamOptim(**cfg['optimizer'], params=model.parameters())
agent = Agent(model=model, optim=optim, **cfg['agent'])
cp_iter = cfg['train']['checkpoint_every']
log_iter = cfg['train']['log_every']
n_end = cfg['train']['steps']
cp_name = cfg['train']['checkpoint_name']
for n_iter, rollout in zip(trange(n_start, n_end), runner):
agent_log = agent.update(rollout)
if n_iter % log_iter == 0:
log.output({**agent_log, **runner.get_logs()}, n_iter)
if n_iter > n_start and n_iter % cp_iter == 0:
f = cp_name.format(n_iter=n_iter // cp_iter)
torch.save(model.state_dict(), f)
def train(cfg_name, env_name):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'running on {device}')
cfg = load_cfg(cfg_name)
log = Logger(device=device)
if env_name == 'OT':
envs = make_obstacle_tower(cfg['train']['num_env'])
else:
envs = make_vec_envs(env_name + 'NoFrameskip-v4',
cfg['train']['num_env'])
emb = cfg['embedding']
model = ActorCritic(output_size=envs.action_space.n,
device=device,
emb_size=emb['size'])
model.train().to(device=device)
runner = EnvRunner(
rollout_size=cfg['train']['rollout_size'],
envs=envs,
model=model,
device=device,
emb_stack=emb['history_size'],
)
optim = ParamOptim(**cfg['optimizer'], params=model.parameters())
agent = Agent(model=model, optim=optim, **cfg['agent'])
n_start = 0
log_iter = cfg['train']['log_every']
n_end = cfg['train']['steps']
log.log.add_text('env', env_name)
for n_iter, rollout in zip(trange(n_start, n_end), runner):
progress = n_iter / n_end
optim.update(progress)
agent_log = agent.update(rollout, progress)
if n_iter % log_iter == 0:
log.output({**agent_log, **runner.get_logs()}, n_iter)
reward = eval_model(model, envs, emb['history_size'], emb['size'], device)
reward_str = f'{reward.mean():.2f} ± {reward.std():.2f}'
log.log.add_text('final', reward_str)
log.log.close()
def train(cfg_name):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'running on {device}')
with open(f'config/{cfg_name}.yaml') as f:
cfg = yaml.load(f)
if cfg['train'].get('resume', False):
n_start, fname = find_checkpoint(cfg)
else:
n_start, fname = 0, None
eps = DecayingEpsilon(**cfg['exploration'], n_iter=n_start)
log = Logger(eps=eps, device=device)
envs = get_envs(cfg, device)
buffer = get_buffer(cfg, envs, device)
agent = get_agent(cfg, envs, device)
if fname:
agent.load(fname)
agent.init_target()
env_runner = EnvRunner(envs=envs, qf=agent.qf, device=device, eps=eps)
cp_iter = cfg['train']['checkpoint_every']
log_iter = cfg['train']['log_every']
counter = trange(n_start, cfg['train']['steps'] + 1)
for n_iter, env_step in zip(counter, env_runner):
eps.update(n_iter)
buffer.append(env_step)
if env_step.get('ep_info') is not None:
log.output(env_step['ep_info'], n_iter)
lr = agent.optim.step_lr(n_iter)
if lr:
log.output({'lr': lr}, n_iter)
if len(buffer) > cfg['buffer']['warmup']:
to_log = agent.update(buffer.sample())
if n_iter % log_iter == 0:
log.stats(n_iter)
log.output(to_log, n_iter)
if n_iter > n_start and n_iter % cp_iter == 0:
fname = cfg['train']['checkpoint_name'].format(n_iter=n_iter //
cp_iter)
agent.save(fname)