def testConcat(self):
b1 = SampleBatch({"a": np.array([1, 2, 3]), "b": np.array([4, 5, 6])})
b2 = SampleBatch({"a": np.array([1]), "b": np.array([4])})
b3 = SampleBatch({"a": np.array([1]), "b": np.array([5])})
b12 = b1.concat(b2)
self.assertEqual(b12["a"].tolist(), [1, 2, 3, 1])
self.assertEqual(b12["b"].tolist(), [4, 5, 6, 4])
b = SampleBatch.concat_samples([b1, b2, b3])
self.assertEqual(b["a"].tolist(), [1, 2, 3, 1, 1])
self.assertEqual(b["b"].tolist(), [4, 5, 6, 4, 5])
def sample(self):
samples_dict = {"observations": [], "rewards": []}
for i in range(self._sample_count):
samples_dict["observations"].append(
self.obs_filter(np.random.randn()))
samples_dict["rewards"].append(self.rew_filter(np.random.randn()))
return SampleBatch(samples_dict)
def training_workflow(config, reporter):
# Setup policy and policy evaluation actors
env = gym.make("CartPole-v0")
policy = CustomPolicy(env.observation_space, env.action_space, {})
workers = [
PolicyEvaluator.as_remote().remote(lambda c: gym.make("CartPole-v0"),
CustomPolicy)
for _ in range(config["num_workers"])
]
for _ in range(config["num_iters"]):
# Broadcast weights to the policy evaluation workers
weights = ray.put({"default_policy": policy.get_weights()})
for w in workers:
w.set_weights.remote(weights)
# Gather a batch of samples
T1 = SampleBatch.concat_samples(
ray.get([w.sample.remote() for w in workers]))
print("DEBUG* BATCH ************************")
print(T1)
print("DEBUG*************************")
# Improve the policy using the T1 batch
policy.learn_on_batch(T1)
reporter(**collect_metrics(remote_evaluators=workers))
def training_workflow(config, reporter):
from gym.spaces import Box
import numpy as np
env_maker = get_env_maker(GazeboEnv)
env, agents = env_maker(config['env_config'], return_agents=True)
space = Box(low=-np.ones(2), high=np.ones(2))
# pdb.set_trace()
replay_buffers = {
agent_id: ReplayBuffer(config.get('buffer_size', 1000))
for agent_id in agents
}
policy = {
k: (RandomPolicy, a.observation_space, a.action_space, {})
for k, a in agents.items()
}
worker = RolloutWorker(lambda x: env,
policy=policy,
batch_steps=32,
policy_mapping_fn=lambda x: x,
episode_horizon=20)
for i in range(config['num_iters']):
T1 = SampleBatch.concat_samples([worker.sample()])
for agent_id, batch in T1.policy_batches.items():
for row in batch.rows():
replay_buffers[agent_id].add(row['obs'],
row['actions'],
row['rewards'],
row['new_obs'],
row['dones'],
weight=None)
pdb.set_trace()
def training_workflow(config, reporter):
# Setup policy and policy evaluation actors
env = gym.make("CartPole-v0")
policy = CustomPolicy(env.observation_space, env.action_space, {})
workers = [
PolicyEvaluator.as_remote().remote(lambda c: gym.make("CartPole-v0"),
CustomPolicy)
for _ in range(config["num_workers"])
]
for _ in range(config["num_iters"]):
# Broadcast weights to the policy evaluation workers
weights = ray.put({"default_policy": policy.get_weights()})
for w in workers:
w.set_weights.remote(weights)
# Gather a batch of samples
T1 = SampleBatch.concat_samples(
ray.get([w.sample.remote() for w in workers]))
# Update the remote policy replicas and gather another batch of samples
new_value = policy.w * 2.0
for w in workers:
w.for_policy.remote(lambda p: p.update_some_value(new_value))
# Gather another batch of samples
T2 = SampleBatch.concat_samples(
ray.get([w.sample.remote() for w in workers]))
# Improve the policy using the T1 batch
policy.learn_on_batch(T1)
# Do some arbitrary updates based on the T2 batch
policy.update_some_value(sum(T2["rewards"]))
reporter(**collect_metrics(remote_evaluators=workers))
def testConcat(self):
b1 = SampleBatch({"a": np.array([1, 2, 3]), "b": np.array([4, 5, 6])})
b2 = SampleBatch({"a": np.array([1]), "b": np.array([4])})
b3 = SampleBatch({"a": np.array([1]), "b": np.array([5])})
b12 = b1.concat(b2)
self.assertEqual(b12["a"].tolist(), [1, 2, 3, 1])
self.assertEqual(b12["b"].tolist(), [4, 5, 6, 4])
b = SampleBatch.concat_samples([b1, b2, b3])
self.assertEqual(b["a"].tolist(), [1, 2, 3, 1, 1])
self.assertEqual(b["b"].tolist(), [4, 5, 6, 4, 5])
def make_sample_batch(i):
return SampleBatch({
"actions": np.array([i, i, i]),
"obs": np.array([i, i, i])
})
import glob
import numpy as np
import os
import shutil
import tempfile
import time
import unittest
import ray
from ray.rllib.agents.pg import PGAgent
from ray.rllib.evaluation import SampleBatch
from ray.rllib.offline import IOContext, JsonWriter, JsonReader
from ray.rllib.offline.json_writer import _to_json
SAMPLES = SampleBatch({
"actions": np.array([1, 2, 3]),
"obs": np.array([4, 5, 6])
})
def make_sample_batch(i):
return SampleBatch({
"actions": np.array([i, i, i]),
"obs": np.array([i, i, i])
})
class AgentIOTest(unittest.TestCase):
def setUp(self):
self.test_dir = tempfile.mkdtemp()
def tearDown(self):
import tempfile
import time
import unittest
import ray
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.agents.pg.pg_policy_graph import PGTFPolicy
from ray.rllib.evaluation import SampleBatch
from ray.rllib.offline import IOContext, JsonWriter, JsonReader
from ray.rllib.offline.json_writer import _to_json
from ray.rllib.tests.test_multi_agent_env import MultiCartpole
from ray.tune.registry import register_env
SAMPLES = SampleBatch({
"actions": np.array([1, 2, 3, 4]),
"obs": np.array([4, 5, 6, 7]),
"eps_id": [1, 1, 2, 3],
})
def make_sample_batch(i):
return SampleBatch({
"actions": np.array([i, i, i]),
"obs": np.array([i, i, i])
})
class AgentIOTest(unittest.TestCase):
def setUp(self):
self.test_dir = tempfile.mkdtemp()
