def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch|MultiAgentBatch from evaluating the current policies.
"""
batches = [self.sampler.get_data()]
steps_so_far = batches[0].count
# In truncate_episodes mode, never pull more than 1 batch per env.
# This avoids over-running the target batch size.
if self.batch_mode == "truncate_episodes":
max_batches = self.num_envs
else:
max_batches = float("inf")
while steps_so_far < self.batch_steps and len(batches) < max_batches:
batch = self.sampler.get_data()
steps_so_far += batch.count
batches.append(batch)
batches.extend(self.sampler.get_extra_batches())
batch = batches[0].concat_samples(batches)
if self.compress_observations:
if isinstance(batch, MultiAgentBatch):
for data in batch.policy_batches.values():
data["obs"] = [pack(o) for o in data["obs"]]
data["new_obs"] = [pack(o) for o in data["new_obs"]]
else:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch|MultiAgentBatch from evaluating the current policies.
"""
batches = [self.sampler.get_data()]
steps_so_far = batches[0].count
while steps_so_far < self.batch_steps:
batch = self.sampler.get_data()
steps_so_far += batch.count
batches.append(batch)
batch = batches[0].concat_samples(batches)
if self.compress_observations:
if isinstance(batch, MultiAgentBatch):
for data in batch.policy_batches.values():
data["obs"] = [pack(o) for o in data["obs"]]
data["new_obs"] = [pack(o) for o in data["new_obs"]]
else:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])):
for key in columns:
if key in self.data:
if bulk:
self.data[key] = pack(self.data[key])
else:
self.data[key] = np.array(
[pack(o) for o in self.data[key]])
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch|MultiAgentBatch from evaluating the current policies.
"""
if log_once("sample_start"):
logger.info("Generating sample batch of size {}".format(
self.sample_batch_size))
batches = [self.input_reader.next()]
steps_so_far = batches[0].count
# In truncate_episodes mode, never pull more than 1 batch per env.
# This avoids over-running the target batch size.
if self.batch_mode == "truncate_episodes":
max_batches = self.num_envs
else:
max_batches = float("inf")
while steps_so_far < self.sample_batch_size and len(
batches) < max_batches:
batch = self.input_reader.next()
steps_so_far += batch.count
batches.append(batch)
batch = batches[0].concat_samples(batches)
if self.callbacks.get("on_sample_end"):
self.callbacks["on_sample_end"]({
"evaluator": self,
"samples": batch
})
# Always do writes prior to compression for consistency and to allow
# for better compression inside the writer.
self.output_writer.write(batch)
# Do off-policy estimation if needed
if self.reward_estimators:
for sub_batch in batch.split_by_episode():
for estimator in self.reward_estimators:
estimator.process(sub_batch)
if log_once("sample_end"):
logger.info("Completed sample batch:\n\n{}\n".format(
summarize(batch)))
if self.compress_observations:
if isinstance(batch, MultiAgentBatch):
for data in batch.policy_batches.values():
data["obs"] = [pack(o) for o in data["obs"]]
data["new_obs"] = [pack(o) for o in data["new_obs"]]
else:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def _compress_in_place(path, value):
if path[0] not in columns:
return
curr = self
for i, p in enumerate(path):
if i == len(path) - 1:
if bulk:
curr[p] = pack(value)
else:
curr[p] = np.array([pack(o) for o in value])
curr = curr[p]
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch|MultiAgentBatch from evaluating the current policies.
"""
batches = [self.input_reader.next()]
steps_so_far = batches[0].count
# In truncate_episodes mode, never pull more than 1 batch per env.
# This avoids over-running the target batch size.
if self.batch_mode == "truncate_episodes":
max_batches = self.num_envs
else:
max_batches = float("inf")
while steps_so_far < self.sample_batch_size and len(
batches) < max_batches:
batch = self.input_reader.next()
steps_so_far += batch.count
batches.append(batch)
batch = batches[0].concat_samples(batches)
if self.callbacks.get("on_sample_end"):
self.callbacks["on_sample_end"]({
"evaluator": self,
"samples": batch
})
# Always do writes prior to compression for consistency and to allow
# for better compression inside the writer.
self.output_writer.write(batch)
# Do off-policy estimation if needed
if self.reward_estimators:
for sub_batch in batch.split_by_episode():
for estimator in self.reward_estimators:
estimator.process(sub_batch)
if self.compress_observations:
if isinstance(batch, MultiAgentBatch):
for data in batch.policy_batches.values():
data["obs"] = [pack(o) for o in data["obs"]]
data["new_obs"] = [pack(o) for o in data["new_obs"]]
else:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch from evaluating the current policies.
"""
batch = self.policy_map["default"].postprocess_trajectory(
self.sampler.get_data())
if self.compress_observations:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def compress(self,
bulk: bool = False,
columns: Set[str] = frozenset(["obs", "new_obs"])) -> None:
"""Compresses the data buffers (by column) in place.
Args:
bulk (bool): Whether to compress across the batch dimension (0)
as well. If False will compress n separate list items, where n
is the batch size.
columns (Set[str]): The columns to compress. Default: Only
compress the obs and new_obs columns.
"""
for key in columns:
if key in self.keys():
if bulk:
self[key] = pack(self[key])
else:
self[key] = np.array([pack(o) for o in self[key]])
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch|MultiAgentBatch from evaluating the current policies.
"""
batches = [self.input_reader.next()]
steps_so_far = batches[0].count
# In truncate_episodes mode, never pull more than 1 batch per env.
# This avoids over-running the target batch size.
if self.batch_mode == "truncate_episodes":
max_batches = self.num_envs
else:
max_batches = float("inf")
while steps_so_far < self.sample_batch_size and len(
batches) < max_batches:
batch = self.input_reader.next()
steps_so_far += batch.count
batches.append(batch)
batch = batches[0].concat_samples(batches)
if self.callbacks.get("on_sample_end"):
self.callbacks["on_sample_end"]({
"evaluator": self,
"samples": batch
})
# Always do writes prior to compression for consistency and to allow
# for better compression inside the writer.
self.output_writer.write(batch)
if self.compress_observations:
if isinstance(batch, MultiAgentBatch):
for data in batch.policy_batches.values():
data["obs"] = [pack(o) for o in data["obs"]]
data["new_obs"] = [pack(o) for o in data["new_obs"]]
else:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def sample(self):
"""Evaluate the current policies and return a batch of experiences.
Return:
SampleBatch from evaluating the current policies.
"""
batches = [self.sampler.get_data()]
steps_so_far = batches[0].count
while steps_so_far < self.batch_steps:
batch = self.sampler.get_data()
steps_so_far += batch.count
batches.append(batch)
batch = SampleBatch.concat_samples(batches)
if self.compress_observations:
batch["obs"] = [pack(o) for o in batch["obs"]]
batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
return batch
def sample(self):
obs, actions, rewards, new_obs, dones = [], [], [], [], []
for _ in range(
self.config["sample_batch_size"] + self.config["n_step"] - 1):
ob, act, rew, ob1, done = self._step(self.global_timestep)
obs.append(ob)
actions.append(act)
rewards.append(rew)
new_obs.append(ob1)
dones.append(done)
# N-step Q adjustments
if self.config["n_step"] > 1:
# Adjust for steps lost from truncation
self.local_timestep -= (self.config["n_step"] - 1)
adjust_nstep(self.config["n_step"], self.config["gamma"], obs,
actions, rewards, new_obs, dones)
batch = SampleBatch({
"obs": [pack(np.array(o)) for o in obs],
"actions": actions,
"rewards": rewards,
"new_obs": [pack(np.array(o)) for o in new_obs],
"dones": dones,
"weights": np.ones_like(rewards)
})
assert (batch.count == self.config["sample_batch_size"])
# Prioritize on the worker side
if self.config["worker_side_prioritization"]:
td_errors = self.ddpg_graph.compute_td_error(
self.sess, obs, batch["actions"], batch["rewards"], new_obs,
batch["dones"], batch["weights"])
new_priorities = (
np.abs(td_errors) + self.config["prioritized_replay_eps"])
batch.data["weights"] = new_priorities
return batch
def sample(self):
obs, actions, rewards, new_obs, dones = [], [], [], [], []
for _ in range(
self.config["sample_batch_size"] + self.config["n_step"] - 1):
ob, act, rew, ob1, done = self._step(self.global_timestep)
obs.append(ob)
actions.append(act)
rewards.append(rew)
new_obs.append(ob1)
dones.append(done)
# N-step Q adjustments
if self.config["n_step"] > 1:
# Adjust for steps lost from truncation
self.local_timestep -= (self.config["n_step"] - 1)
adjust_nstep(
self.config["n_step"], self.config["gamma"],
obs, actions, rewards, new_obs, dones)
batch = SampleBatch({
"obs": [pack(np.array(o)) for o in obs], "actions": actions,
"rewards": rewards,
"new_obs": [pack(np.array(o)) for o in new_obs], "dones": dones,
"weights": np.ones_like(rewards)})
assert (batch.count == self.config["sample_batch_size"])
# Prioritize on the worker side
if self.config["worker_side_prioritization"]:
td_errors = self.dqn_graph.compute_td_error(
self.sess, obs, batch["actions"], batch["rewards"],
new_obs, batch["dones"], batch["weights"])
new_priorities = (
np.abs(td_errors) + self.config["prioritized_replay_eps"])
batch.data["weights"] = new_priorities
return batch
def _to_jsonable(v, compress):
if compress:
return str(pack(v))
elif isinstance(v, np.ndarray):
return v.tolist()
return v
def _to_jsonable(v, compress):
if compress:
return str(pack(v))
elif isinstance(v, np.ndarray):
return v.tolist()
return v
def worker_rollout(ps, replay_buffer, opt, worker_index):
agent = Actor(opt, job="worker")
keys = agent.get_weights()[0]
np.random.seed()
rand_buff1 = np.random.choice(opt.num_buffers, 1)[0]
random_steps = 0
while True:
# ------ env set up ------
env = TradingEnv()
# env = Wrapper(env, opt.action_repeat, opt.reward_scale)
# ------ env set up end ------
o_queue = deque([], maxlen=opt.Ln + 1)
a_r_d_queue = deque([], maxlen=opt.Ln)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
ep_score, ep_target_bias = 0, 0
if opt.model == "cnn":
compressed_o = pack(o)
o_queue.append((compressed_o,))
else:
o_queue.append((o,))
t_queue = 1
weights = ray.get(ps.pull.remote(keys))
agent.set_weights(keys, weights)
# for a_l_ratio control
np.random.seed()
rand_buff = np.random.choice(opt.num_buffers, 1)[0]
last_learner_steps, last_actor_steps, _size = ray.get(replay_buffer[rand_buff].get_counts.remote())
while True:
# don't need to random sample action if load weights from local.
if random_steps > opt.start_steps or opt.weights_file or opt.recover:
a = agent.get_action(o, deterministic=False)
else:
a = env.action_space.sample()
random_steps += 1
# Step the env
o2, r, d, info = env.step(a)
ep_ret += r
ep_score += info['score']
ep_target_bias += info['target_bias']
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
# d = False if ep_len*opt.action_repeat >= opt.max_ep_len else d
o = o2
a_r_d_queue.append((a, r, d,))
if opt.model == "cnn":
compressed_o2 = pack(o2)
o_queue.append((compressed_o2,))
else:
o_queue.append((o2,))
# scheme 1:
# TODO and t_queue % 2 == 0: %1 lead to q smaller
# TODO
if t_queue >= opt.Ln and t_queue % opt.save_freq == 0:
replay_buffer[np.random.choice(opt.num_buffers, 1)[0]].store.remote(o_queue, a_r_d_queue, worker_index)
t_queue += 1
# End of episode. Training (ep_len times).
# if d or (ep_len * opt.action_repeat >= opt.max_ep_len):
if d or ep_len > opt.max_ep_len:
sample_times, steps, _ = ray.get(replay_buffer[0].get_counts.remote())
# print('rollout ep_len:', ep_len * opt.action_repeat, 'ep_score:', ep_score,
# 'ep_target_bias:', ep_target_bias)
if steps > opt.start_steps:
# update parameters every episode
weights = ray.get(ps.pull.remote(keys))
agent.set_weights(keys, weights)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
t_queue = 1
if opt.model == "cnn":
compressed_o = pack(o)
o_queue.append((compressed_o,))
else:
o_queue.append((o,))
# for a_l_ratio control
learner_steps, actor_steps, _size = ray.get(replay_buffer[rand_buff].get_counts.remote())
while (actor_steps - last_actor_steps) / (
learner_steps - last_learner_steps + 1) > opt.a_l_ratio and last_learner_steps > 0:
time.sleep(1)
learner_steps, actor_steps, _size = ray.get(replay_buffer[rand_buff].get_counts.remote())
def _to_jsonable(v, compress: bool) -> Any:
if compress and compression_supported():
return str(pack(v))
elif isinstance(v, np.ndarray):
return v.tolist()
return v
def worker_rollout(ps, replay_buffer, opt, worker_index):
agent = Actor(opt, job="worker")
keys = agent.get_weights()[0]
filling_steps = 0
while True:
# ------ env set up ------
env = Wrapper(gym.make(opt.env_name), opt.obs_noise, opt.act_noise,
opt.reward_scale, 3)
# ------ env set up end ------
################################## deques
o_queue = deque([], maxlen=opt.Ln + 1)
a_r_d_queue = deque([], maxlen=opt.Ln)
################################## deques
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
################################## deques reset
t_queue = 1
if opt.model == "cnn":
compressed_o = pack(o)
o_queue.append((compressed_o, ))
else:
o_queue.append((o, ))
################################## deques reset
weights = ray.get(ps.pull.remote(keys))
agent.set_weights(keys, weights)
while True:
# don't need to random sample action if load weights from local.
if filling_steps > opt.start_steps or opt.weights_file:
a = agent.get_action(o, deterministic=False)
else:
a = env.action_space.sample()
filling_steps += 1
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
# d = False if ep_len*opt.action_repeat >= opt.max_ep_len else d
o = o2
#################################### deques store
a_r_d_queue.append((
a,
r,
d,
))
if opt.model == "cnn":
compressed_o2 = pack(o2)
o_queue.append((compressed_o2, ))
else:
o_queue.append((o2, ))
# scheme 1:
# TODO and t_queue % 2 == 0: %1 lead to q smaller
# TODO
if t_queue >= opt.Ln and t_queue % opt.save_freq == 0:
replay_buffer[np.random.choice(opt.num_buffers,
1)[0]].store.remote(
o_queue, a_r_d_queue,
worker_index)
t_queue += 1
#################################### deques store
# End of episode. Training (ep_len times).
if d or (ep_len * opt.action_repeat >= opt.max_ep_len):
# TODO
sample_times, steps, _ = ray.get(
replay_buffer[0].get_counts.remote())
print('rollout_ep_len:', ep_len * opt.action_repeat,
'rollout_ep_ret:', ep_ret)
if steps > opt.start_steps:
# update parameters every episode
weights = ray.get(ps.pull.remote(keys))
agent.set_weights(keys, weights)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
################################## deques reset
t_queue = 1
if opt.model == "cnn":
compressed_o = pack(o)
o_queue.append((compressed_o, ))
else:
o_queue.append((o, ))
