def job(
random_seed: int,
base_dir: Path,
theta_min: float,
theta_max: float,
theta_dot_min: float,
theta_dot_max: float,
):
rng = random.PRNGKey(random_seed)
rng, train_rng = random.split(rng)
callback_rngs = random.split(rng, num_episodes)
params = [None]
tracking_params = [None]
train_reward_per_episode = []
policy_value_per_episode = []
episode_lengths = []
elapsed_per_episode = []
def callback(info):
episode = info['episode']
params[0] = info["optimizer"].value
tracking_params[0] = info["tracking_params"]
policy_value = run_ddpg.eval_policy(callback_rngs[episode],
info["optimizer"].value[0])
train_reward_per_episode.append(info['reward'])
policy_value_per_episode.append(policy_value)
episode_lengths.append(info["episode_length"])
elapsed_per_episode.append(info["elapsed"])
run_ddpg.train(
train_rng,
num_episodes,
lambda t, s: lax.bitwise_or(
lax.ge(t, config.episode_length),
lax.bitwise_or(
lax.le(s[0], theta_min),
lax.bitwise_or(
lax.ge(s[0], theta_max),
lax.bitwise_or(lax.le(s[1], theta_dot_min),
lax.ge(s[1], theta_dot_max))))),
callback,
)
with (base_dir / f"seed={random_seed}.pkl").open(mode="wb") as f:
pickle.dump(
{
"final_params": params[0],
"final_tracking_params": tracking_params[0],
"train_reward_per_episode": train_reward_per_episode,
"policy_value_per_episode": policy_value_per_episode,
"episode_lengths": episode_lengths,
"elapsed_per_episode": elapsed_per_episode,
}, f)
def nanvar(a,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
dtype=None,
out=None,
ddof=0,
keepdims=False,
where=None):
_check_arraylike("nanvar", a)
lax_internal._check_user_dtype_supported(dtype, "nanvar")
if out is not None:
raise NotImplementedError(
"The 'out' argument to jnp.nanvar is not supported.")
a_dtype, dtype = _var_promote_types(dtypes.dtype(a), dtype)
a_mean = nanmean(a, axis, dtype=a_dtype, keepdims=True, where=where)
centered = _where(lax_internal._isnan(a), 0,
a - a_mean) # double-where trick for gradients.
if dtypes.issubdtype(centered.dtype, np.complexfloating):
centered = lax.real(lax.mul(centered, lax.conj(centered)))
else:
centered = lax.square(centered)
normalizer = sum(lax_internal.bitwise_not(lax_internal._isnan(a)),
axis=axis,
keepdims=keepdims,
where=where)
normalizer = normalizer - ddof
normalizer_mask = lax.le(normalizer, 0)
result = sum(centered, axis, keepdims=keepdims, where=where)
result = _where(normalizer_mask, np.nan, result)
divisor = _where(normalizer_mask, 1, normalizer)
out = lax.div(result, lax.convert_element_type(divisor, result.dtype))
return lax.convert_element_type(out, dtype)
def logpmf(k, p, loc=0):
k, p, loc = jnp._promote_args_inexact("geom.logpmf", k, p, loc)
zero = lax._const(k, 0)
one = lax._const(k, 1)
x = lax.sub(k, loc)
log_probs = xlog1py(lax.sub(x, one), -p) + lax.log(p)
return jnp.where(lax.le(x, zero), -jnp.inf, log_probs)
def cdf(x, loc=0, scale=1):
x, loc, scale = _promote_args_inexact("laplace.cdf", x, loc, scale)
half = _constant_like(x, 0.5)
one = _constant_like(x, 1)
zero = _constant_like(x, 0)
diff = lax.div(lax.sub(x, loc), scale)
return lax.select(lax.le(diff, zero), lax.mul(half, lax.exp(diff)),
lax.sub(one, lax.mul(half, lax.exp(lax.neg(diff)))))
def cond_fun(state):
i, j, _ = state
return lax.le(j, i)
def isclose(a, b, rtol=1e-05, atol=1e-08):
a, b = _promote_args("isclose", a, b)
rtol = lax.convert_element_type(rtol, _dtype(a))
atol = lax.convert_element_type(atol, _dtype(a))
return lax.le(lax.abs(lax.sub(a, b)),
lax.add(atol, lax.mul(rtol, lax.abs(b))))
