def test_seed(self):
sampler = SerialSampler(
EnvCls=gym_make,
env_kwargs={"id": "MountainCarContinuous-v0"},
batch_T=1,
batch_B=1,
)
agent = SacAgent(pretrain_std=0.0)
agent.give_min_itr_learn(10000)
set_seed(0)
sampler.initialize(agent, seed=0)
samples_1 = sampler.obtain_samples(0)
set_seed(0)
sampler.initialize(agent, seed=0)
samples_2 = sampler.obtain_samples(0)
# Dirty hack to compare objects containing tensors.
self.assertEqual(str(samples_1), str(samples_2))
samples_3 = sampler.obtain_samples(0)
self.assertNotEqual(samples_1, samples_3)
def estimateForState(s):
cpus = list(range(C.N_PARALLEL))
affinity = dict(cuda_idx=C.CUDA_IDX, workers_cpus=cpus)
agent_ = CategoricalPgAgent(
AcrobotNet, initial_model_state_dict=agent.state_dict())
sampler = SerialSampler(
EnvCls=rlpyt_make,
env_kwargs=dict(id=C.ENV,
reward=rewardFn,
internalStateFn=C.INTERNAL_STATE_FN,
s0=s),
batch_T=C.HORIZON,
batch_B=C.BATCH_B,
max_decorrelation_steps=0,
)
sampler.initialize(agent=agent_, affinity=affinity, seed=C.SEED)
_, traj_info = sampler.obtain_samples(0)
returns = [t['DiscountedReturn'] for t in traj_info]
return np.mean(returns)
def log_diagnostics(itr, algo, agent, sampler):
mp: ProMP = agent.model.promp
mu, cov = mp.mu_and_cov_w
std = cov.diagonal(dim1=-2, dim2=-1).sqrt().detach().numpy()
# for i in range(std.shape[0]):
# record_tabular('agent/std{}'.format(i), std[i])
record_tabular_misc_stat('AgentCov', std)
record_tabular_misc_stat('AgentMu', mu.detach().numpy())
runner = MinibatchRlWithLog(algo=args.get_ppo_from_options(options),
agent=agent,
sampler=sampler,
log_traj_window=32,
n_steps=int(500000 * sampler.batch_size),
log_interval_steps=int(horizon * 32),
log_diagnostics_fun=log_diagnostics)
if not args.is_evaluation(options):
with args.get_default_context(options):
runner.train()
else:
runner.startup()
while True:
sampler.obtain_samples(0)
print(np.sum(sampler.samples_np.env.reward, 0))
EnvProMP.plot_states(sampler.samples_np.env.observation,
sampler.samples_np.agent.action,
max_obs=64)