def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built with both frameworks."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
num_iterations = 2
for _ in framework_iterator(config, frameworks=["tf", "eager"]):
# Rainbow.
rainbow_config = config.copy()
rainbow_config["num_atoms"] = 10
rainbow_config["noisy"] = True
rainbow_config["double_q"] = True
rainbow_config["dueling"] = True
rainbow_config["n_step"] = 5
trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
# double-dueling DQN.
plain_config = config.copy()
trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built on all frameworks."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 2
num_iterations = 1
for _ in framework_iterator(config):
# Double-dueling DQN.
print("Double-dueling")
plain_config = config.copy()
trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
# Rainbow.
print("Rainbow")
rainbow_config = config.copy()
rainbow_config["num_atoms"] = 10
rainbow_config["noisy"] = True
rainbow_config["double_q"] = True
rainbow_config["dueling"] = True
rainbow_config["n_step"] = 5
trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built on all frameworks."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
num_iterations = 2
for fw in framework_iterator(config):
# double-dueling DQN.
plain_config = config.copy()
trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
# Rainbow.
# TODO(sven): Add torch once DQN-torch supports distributional-Q.
if fw == "torch":
continue
rainbow_config = config.copy()
rainbow_config["num_atoms"] = 10
rainbow_config["noisy"] = True
rainbow_config["double_q"] = True
rainbow_config["dueling"] = True
rainbow_config["n_step"] = 5
trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built on all frameworks."""
num_iterations = 1
config = dqn.dqn.DQNConfig().rollouts(num_rollout_workers=2)
for _ in framework_iterator(config, with_eager_tracing=True):
# Double-dueling DQN.
print("Double-dueling")
plain_config = deepcopy(config)
trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(trainer)
trainer.stop()
# Rainbow.
print("Rainbow")
rainbow_config = deepcopy(config).training(num_atoms=10,
noisy=True,
double_q=True,
dueling=True,
n_step=5)
trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(trainer)
trainer.stop()
def main(params):
for (key, value) in params.items():
print("Parameter {} is set to {}".format(key, value))
if not params["use_gym_env"]:
register_env(params["env_name"], get_env_creator(params["env_name"]))
if params["model"] == "DQN":
print(3)
from ray.rllib.agents import dqn
ray.init()
print(4)
config = dqn.DEFAULT_CONFIG.copy()
config["framework"] = params["framework"]
env = str(params["env_name"])
print(5)
trainer = dqn.DQNTrainer(config=config, env=env)
print(6)
for i in range(100):
print(trainer.train()['episode_reward_mean'])
if params["model"] == "PPO":
ray.init()
ModelCatalog.register_custom_model("my_model", TorchCustomModel)
trainer = get_trainer_from_params(params)
if params["train"]:
for i in range(params['num_training_iters']):
print("starting training iteration {}".format(i))
trainer.train()
if i == params['num_training_iters'] - 1:
checkpoint_path = trainer.save()
print(checkpoint_path)
def test_evaluation_option(self):
config = dqn.DEFAULT_CONFIG.copy()
config.update({
"env": "CartPole-v0",
"evaluation_interval": 2,
"evaluation_num_episodes": 2,
"evaluation_config": {
"gamma": 0.98,
},
# Use a custom callback that asserts that we are running the
# configured exact number of episodes per evaluation.
"callbacks": AssertNumEvalEpisodesCallback,
})
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = dqn.DQNTrainer(config=config)
# Given evaluation_interval=2, r0, r2, r4 should not contain
# evaluation metrics, while r1, r3 should.
r0 = trainer.train()
print(r0)
r1 = trainer.train()
print(r1)
r2 = trainer.train()
print(r2)
r3 = trainer.train()
print(r3)
trainer.stop()
self.assertFalse("evaluation" in r0)
self.assertTrue("evaluation" in r1)
self.assertFalse("evaluation" in r2)
self.assertTrue("evaluation" in r3)
self.assertTrue("episode_reward_mean" in r1["evaluation"])
self.assertNotEqual(r1["evaluation"], r3["evaluation"])
def create_agent(args):
"""Create DQN agent.
Args:
args (argparse.Namespace): argparse arguments.
Returns:
agent (ray.rllib.agents.trainer_template.DQN): DQN agent.
"""
# Custom configuration
config = dqn.DEFAULT_CONFIG.copy()
config["double_q"] = True
config["dueling"] = True
config["framework"] = "torch"
config["horizon"] = 1150
config["num_gpus"] = 1
config["num_workers"] = 19
config["train_batch_size"] = 128
# Agent creation
agent = dqn.DQNTrainer(env=MissileCommand, config=config)
# To optionally load a checkpoint
if args.checkpoint:
agent.restore(args.checkpoint)
# Print model
if args.verbose > 0:
model = agent.get_policy().model
if config["framework"] == "tf":
print(type(model.base_model.summary()))
elif config["framework"] == "torch":
print(model)
return agent
def test_traj_view_normal_case(self):
"""Tests, whether Model and Policy return the correct ViewRequirements.
"""
config = dqn.DEFAULT_CONFIG.copy()
for _ in framework_iterator(config):
trainer = dqn.DQNTrainer(
config,
env="ray.rllib.examples.env.debug_counter_env.DebugCounterEnv")
policy = trainer.get_policy()
view_req_model = policy.model.inference_view_requirements
view_req_policy = policy.view_requirements
assert len(view_req_model) == 1, view_req_model
assert len(view_req_policy) == 8, view_req_policy
for key in [
SampleBatch.OBS,
SampleBatch.ACTIONS,
SampleBatch.REWARDS,
SampleBatch.DONES,
SampleBatch.NEXT_OBS,
SampleBatch.EPS_ID,
SampleBatch.AGENT_INDEX,
"weights",
]:
assert key in view_req_policy
# None of the view cols has a special underlying data_col,
# except next-obs.
if key != SampleBatch.NEXT_OBS:
assert view_req_policy[key].data_col is None
else:
assert view_req_policy[key].data_col == SampleBatch.OBS
assert view_req_policy[key].shift == 1
trainer.stop()
def test_evaluation_option_always_attach_eval_metrics(self):
config = dqn.DEFAULT_CONFIG.copy()
config.update(
{
"env": "CartPole-v0",
"evaluation_interval": 2,
"evaluation_duration": 2,
"evaluation_duration_unit": "episodes",
"evaluation_config": {
"gamma": 0.98,
},
"always_attach_evaluation_results": True,
# Use a custom callback that asserts that we are running the
# configured exact number of episodes per evaluation.
"callbacks": AssertEvalCallback,
}
)
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = dqn.DQNTrainer(config=config)
# Should always see latest available eval results.
r0 = trainer.train()
r1 = trainer.train()
r2 = trainer.train()
r3 = trainer.train()
trainer.stop()
# Eval results are not available at step 0.
# But step 3 should still have it, even though no eval was
# run during that step.
self.assertTrue("evaluation" in r0)
self.assertTrue("evaluation" in r1)
self.assertTrue("evaluation" in r2)
self.assertTrue("evaluation" in r3)
def test_evaluation_option(self):
config = dqn.DEFAULT_CONFIG.copy()
config.update({
"env": "CartPole-v0",
"evaluation_interval": 2,
"evaluation_num_episodes": 2,
"evaluation_config": {
"gamma": 0.98,
}
})
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = dqn.DQNTrainer(config=config)
# Given evaluation_interval=2, r0, r2, r4 should not contain
# evaluation metrics, while r1, r3 should.
r0 = trainer.train()
print(r0)
r1 = trainer.train()
print(r1)
r2 = trainer.train()
print(r2)
r3 = trainer.train()
print(r3)
trainer.stop()
self.assertFalse("evaluation" in r0)
self.assertTrue("evaluation" in r1)
self.assertFalse("evaluation" in r2)
self.assertTrue("evaluation" in r3)
self.assertTrue("episode_reward_mean" in r1["evaluation"])
self.assertNotEqual(r1["evaluation"], r3["evaluation"])
def test_dqn_fake_multi_gpu_learning(self):
"""Test whether DQNTrainer can learn CartPole w/ faked multi-GPU."""
config = copy.deepcopy(dqn.DEFAULT_CONFIG)
# Fake GPU setup.
config["num_gpus"] = 2
config["_fake_gpus"] = True
# Double batch size (2 GPUs).
config["train_batch_size"] = 64
# Mimic tuned_example for DQN CartPole.
config["n_step"] = 3
config["model"]["fcnet_hiddens"] = [64]
config["model"]["fcnet_activation"] = "linear"
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = dqn.DQNTrainer(config=config, env="CartPole-v0")
num_iterations = 200
learnt = False
for i in range(num_iterations):
results = trainer.train()
print("reward={}".format(results["episode_reward_mean"]))
if results["episode_reward_mean"] > 65.0:
learnt = True
break
assert learnt, \
"DQN multi-GPU (with fake-GPUs) did not learn CartPole!"
trainer.stop()
def get_dqn_car_trainer():
ModelCatalog.register_custom_model("my_model", TorchCustomModel)
config = {
"env": StoppingCar, #
"model": {
"custom_model": "my_model",
"fcnet_hiddens": [16],
"fcnet_activation": "relu"
}, # model config,
# "vf_share_layers": False, # try different lrs
# "vf_clip_param": 100,
"lr": 0.001,
# "clip_rewards": False, # 500*1000,
"grad_clip": 2500,
# "worker_side_prioritization": True,
"num_workers": 8, # parallelism
# "batch_mode": "complete_episodes",
"batch_mode": "truncate_episodes",
"rollout_fragment_length": 2000,
"num_envs_per_worker": 10,
"train_batch_size": 4000,
"hiddens": [16],
"framework": "torch",
"horizon": 8000,
"evaluation_config": {
# Example: overriding env_config, exploration, etc:
# "env_config": {...},
"explore": False
},
}
trainer = dqn.DQNTrainer(config=config)
return trainer, config
def test_on_sub_environment_created_with_remote_envs(self):
config = {
"env": "CartPole-v1",
# Make each sub-environment a ray actor.
"remote_worker_envs": True,
# Create 4 sub-environments (ray remote actors) per remote
# worker.
"num_envs_per_worker": 4,
# Create 2 remote workers.
"num_workers": 2,
"callbacks": OnSubEnvironmentCreatedCallback,
}
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = dqn.DQNTrainer(config=config)
# Fake the counter on the local worker (doesn't have an env) and
# set it to -1 so the below `foreach_worker()` won't fail.
trainer.workers.local_worker().sum_sub_env_vector_indices = -1
# Get sub-env vector index sums from the 2 remote workers:
sum_sub_env_vector_indices = trainer.workers.foreach_worker(
lambda w: w.sum_sub_env_vector_indices)
# Local worker has no environments -> Expect the -1 special
# value returned by the above lambda.
self.assertTrue(sum_sub_env_vector_indices[0] == -1)
# Both remote workers (index 1 and 2) have a vector index counter
# of 6 (sum of vector indices: 0 + 1 + 2 + 3).
self.assertTrue(sum_sub_env_vector_indices[1] == 6)
self.assertTrue(sum_sub_env_vector_indices[2] == 6)
trainer.stop()
def trainDqn(numIter):
"""
train
"""
ray.shutdown()
ray.init()
config = createConfig()
trainer = dqn.DQNTrainer(config=config, env=HiLoPricingEnv)
for i in range(numIter):
print("\n**** next iteration " + str(i))
HiLoPricingEnv.count = 0
result = trainer.train()
print(pretty_print(result))
print("env reset count " + str(HiLoPricingEnv.count))
policy = trainer.get_policy()
weights = policy.get_weights()
#print("policy weights")
#print(weights)
model = policy.model
#summary = model.base_model.summary()
#print("model summary")
#print(weights)
return trainer
def __new__(cls, config={}):
name = config.pop('agent', None)
if name == "DQN":
return dqn.DQNTrainer(config=config)
elif name == "PPO":
return ppo.APPOTrainer(config=config)
else:
raise Exception("{} agent is not supported".format(name))
def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built with both frameworks."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
# tf.
config["eager"] = True
trainer = dqn.DQNTrainer(config=config, env="CartPole-v0")
num_iterations = 2
for i in range(num_iterations):
results = trainer.train()
print(results)
config["eager"] = False
trainer = dqn.DQNTrainer(config=config, env="CartPole-v0")
num_iterations = 2
for i in range(num_iterations):
results = trainer.train()
print(results)
def loadTrainer(path):
"""
load trainer from checkpoint
"""
ray.shutdown()
ray.init()
config = createConfig()
trainer = dqn.DQNTrainer(config=config, env=HiLoPricingEnv)
trainer.restore(path)
return trainer
def test_dqn_compilation(self):
"""Test whether a DQNTrainer can be built with both frameworks."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
# Rainbow.
rainbow_config = config.copy()
rainbow_config["eager"] = False
rainbow_config["num_atoms"] = 10
rainbow_config["noisy"] = True
rainbow_config["double_q"] = True
rainbow_config["dueling"] = True
rainbow_config["n_step"] = 5
trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
num_iterations = 2
for i in range(num_iterations):
results = trainer.train()
print(results)
# tf.
tf_config = config.copy()
tf_config["eager"] = False
trainer = dqn.DQNTrainer(config=tf_config, env="CartPole-v0")
num_iterations = 1
for i in range(num_iterations):
results = trainer.train()
print(results)
# Eager.
eager_config = config.copy()
eager_config["eager"] = True
eager_ctx = eager_mode()
eager_ctx.__enter__()
trainer = dqn.DQNTrainer(config=eager_config, env="CartPole-v0")
num_iterations = 1
for i in range(num_iterations):
results = trainer.train()
print(results)
eager_ctx.__exit__(None, None, None)
def test_leaky_policy(self):
"""Tests, whether our diagnostics tools can detect leaks in a policy."""
config = dqn.DEFAULT_CONFIG.copy()
# Make sure we have an env to test on the local worker.
# Otherwise, `check_memory_leaks` will complain.
config["create_env_on_driver"] = True
config["env"] = "CartPole-v0"
config["multiagent"]["policies"] = {
"default_policy": PolicySpec(policy_class=MemoryLeakingPolicy),
}
trainer = dqn.DQNTrainer(config=config)
results = check_memory_leaks(trainer, to_check={"policy"}, repeats=300)
assert results["policy"]
trainer.stop()
def _discrete_run(self):
from ray import tune
from ray.rllib.agents import dqn
from ray.rllib.agents.dqn import DEFAULT_CONFIG
DEFAULT_CONFIG['framework']='torch'
if self.configs['mode']:
DEFAULT_CONFIG['double_q']=False
else:
DEFAULT_CONFIG['double_q']=True
import ray
AGENT_CONFIG={'dqn':dqn.DQNTrainer,
'ddqn':dqn.DQNTrainer(config=DEFAULT_CONFIG,env='CartPole-v0')}
agent=AGENT_CONFIG[self.configs['algorithm']]
tune.run(agent, config={"env": "CartPole-v0","framework":"torch"})
def train_rllib_policy(config):
"""Trains a DQNTrainer on MsPacman-v0 for n iterations.
Saves the trained Trainer to disk and returns the checkpoint path.
Returns:
str: The saved checkpoint to restore the trainer DQNTrainer from.
"""
# Create trainer from config.
trainer = dqn.DQNTrainer(config=config)
# Train for n iterations, then save.
for _ in range(args.train_iters):
print(trainer.train())
return trainer.save()
def get_rl_agent(agent_name, config, env_to_agent):
if agent_name == A2C:
import ray.rllib.agents.a3c as a2c
agent = a2c.A2CTrainer(config=config, env=env_to_agent)
elif agent_name == A3C:
import ray.rllib.agents.a3c as a3c
agent = a3c.A3CTrainer(config=config, env=env_to_agent)
elif agent_name == BC:
import ray.rllib.agents.marwil as bc
agent = bc.BCTrainer(config=config, env=env_to_agent)
elif agent_name == DQN:
import ray.rllib.agents.dqn as dqn
agent = dqn.DQNTrainer(config=config, env=env_to_agent)
elif agent_name == APEX_DQN:
import ray.rllib.agents.dqn as dqn
agent = dqn.ApexTrainer(config=config, env=env_to_agent)
elif agent_name == IMPALA:
import ray.rllib.agents.impala as impala
agent = impala.ImpalaTrainer(config=config, env=env_to_agent)
elif agent_name == MARWIL:
import ray.rllib.agents.marwil as marwil
agent = marwil.MARWILTrainer(config=config, env=env_to_agent)
elif agent_name == PG:
import ray.rllib.agents.pg as pg
agent = pg.PGTrainer(config=config, env=env_to_agent)
elif agent_name == PPO:
import ray.rllib.agents.ppo as ppo
agent = ppo.PPOTrainer(config=config, env=env_to_agent)
elif agent_name == APPO:
import ray.rllib.agents.ppo as ppo
agent = ppo.APPOTrainer(config=config, env=env_to_agent)
elif agent_name == SAC:
import ray.rllib.agents.sac as sac
agent = sac.SACTrainer(config=config, env=env_to_agent)
elif agent_name == LIN_UCB:
import ray.rllib.contrib.bandits.agents.lin_ucb as lin_ucb
agent = lin_ucb.LinUCBTrainer(config=config, env=env_to_agent)
elif agent_name == LIN_TS:
import ray.rllib.contrib.bandits.agents.lin_ts as lin_ts
agent = lin_ts.LinTSTrainer(config=config, env=env_to_agent)
else:
raise Exception("Not valid agent name")
return agent
def test_traj_view_normal_case(self):
"""Tests, whether Model and Policy return the correct ViewRequirements.
"""
config = dqn.DEFAULT_CONFIG.copy()
config["num_envs_per_worker"] = 10
config["rollout_fragment_length"] = 4
for _ in framework_iterator(config):
trainer = dqn.DQNTrainer(
config,
env="ray.rllib.examples.env.debug_counter_env.DebugCounterEnv")
policy = trainer.get_policy()
view_req_model = policy.model.inference_view_requirements
view_req_policy = policy.view_requirements
assert len(view_req_model) == 1, view_req_model
assert len(view_req_policy) == 8, view_req_policy
for key in [
SampleBatch.OBS,
SampleBatch.ACTIONS,
SampleBatch.REWARDS,
SampleBatch.DONES,
SampleBatch.NEXT_OBS,
SampleBatch.EPS_ID,
SampleBatch.AGENT_INDEX,
"weights",
]:
assert key in view_req_policy
# None of the view cols has a special underlying data_col,
# except next-obs.
if key != SampleBatch.NEXT_OBS:
assert view_req_policy[key].data_col is None
else:
assert view_req_policy[key].data_col == SampleBatch.OBS
assert view_req_policy[key].data_rel_pos == 1
rollout_worker = trainer.workers.local_worker()
sample_batch = rollout_worker.sample()
expected_count = \
config["num_envs_per_worker"] * \
config["rollout_fragment_length"]
assert sample_batch.count == expected_count
for v in sample_batch.data.values():
assert len(v) == expected_count
trainer.stop()
def get_rllib_agent(agent_name, env_name, env, env_to_agent):
config = get_config(env_name, env, 1) if is_rllib_agent(agent_name) else {}
if agent_name == RLLIB_A2C:
import ray.rllib.agents.a3c as a2c
agent = a2c.A2CTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_A3C:
import ray.rllib.agents.a3c as a3c
agent = a3c.A3CTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_BC:
import ray.rllib.agents.marwil as bc
agent = bc.BCTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_DQN:
import ray.rllib.agents.dqn as dqn
agent = dqn.DQNTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_APEX_DQN:
import ray.rllib.agents.dqn as dqn
agent = dqn.ApexTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_IMPALA:
import ray.rllib.agents.impala as impala
agent = impala.ImpalaTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_MARWIL:
import ray.rllib.agents.marwil as marwil
agent = marwil.MARWILTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_PG:
import ray.rllib.agents.pg as pg
agent = pg.PGTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_PPO:
import ray.rllib.agents.ppo as ppo
agent = ppo.PPOTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_APPO:
import ray.rllib.agents.ppo as ppo
agent = ppo.APPOTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_SAC:
import ray.rllib.agents.sac as sac
agent = sac.SACTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_LIN_UCB:
import ray.rllib.contrib.bandits.agents.lin_ucb as lin_ucb
agent = lin_ucb.LinUCBTrainer(config=config, env=env_to_agent)
elif agent_name == RLLIB_LIN_TS:
import ray.rllib.contrib.bandits.agents.lin_ts as lin_ts
agent = lin_ts.LinTSTrainer(config=config, env=env_to_agent)
return agent
def create_agent(args):
"""Create XXX agent.
Args:
args (argparse.Namespace): argparse arguments.
Returns:
agent (ray.rllib.agents.trainer_template.XXX): XXX agent.
"""
# Custom configuration
config = dqn.DEFAULT_CONFIG.copy()
config["double_q"] = True
config["dueling"] = True
config["framework"] = "torch"
config["lr"] = 5e-4
config["num_gpus"] = 1
config["num_workers"] = 1
config["train_batch_size"] = 128
# Custom model
config["model"]["fcnet_activation"] = "relu"
config["model"]["fcnet_hiddens"] = [64, 64]
# Agent creation
agent = dqn.DQNTrainer(env=GymEnv, config=config)
# To optionally load a checkpoint
if args.checkpoint:
agent.restore(args.checkpoint)
# Print model
if args.verbose > 0:
model = agent.get_policy().model
if config["framework"] == "tf":
print(type(model.base_model.summary()))
elif config["framework"] == "torch":
print(model)
return agent
def test_dqn_exploration_and_soft_q_config(self):
"""Tests, whether a DQN Agent outputs exploration/softmaxed actions."""
config = dqn.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["env_config"] = {"is_slippery": False, "map_name": "4x4"}
obs = np.array(0)
# Test against all frameworks.
for _ in framework_iterator(config):
# Default EpsilonGreedy setup.
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
# Setting explore=False should always return the same action.
a_ = trainer.compute_action(obs, explore=False)
for _ in range(50):
a = trainer.compute_action(obs, explore=False)
check(a, a_)
# explore=None (default: explore) should return different actions.
actions = []
for _ in range(50):
actions.append(trainer.compute_action(obs))
check(np.std(actions), 0.0, false=True)
# Low softmax temperature. Behaves like argmax
# (but no epsilon exploration).
config["exploration_config"] = {
"type": "SoftQ",
"temperature": 0.000001
}
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
# Due to the low temp, always expect the same action.
actions = [trainer.compute_action(obs)]
for _ in range(50):
actions.append(trainer.compute_action(obs))
check(np.std(actions), 0.0, decimals=3)
# Higher softmax temperature.
config["exploration_config"]["temperature"] = 1.0
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
# Even with the higher temperature, if we set explore=False, we
# should expect the same actions always.
a_ = trainer.compute_action(obs, explore=False)
for _ in range(50):
a = trainer.compute_action(obs, explore=False)
check(a, a_)
# Due to the higher temp, expect different actions avg'ing
# around 1.5.
actions = []
for _ in range(300):
actions.append(trainer.compute_action(obs))
check(np.std(actions), 0.0, false=True)
# With Random exploration.
config["exploration_config"] = {"type": "Random"}
config["explore"] = True
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
actions = []
for _ in range(300):
actions.append(trainer.compute_action(obs))
check(np.std(actions), 0.0, false=True)
def test_dqn_parameter_noise_exploration(self):
"""Tests, whether a DQN Agent works with ParameterNoise."""
obs = np.array(0)
core_config = dqn.DEFAULT_CONFIG.copy()
core_config["num_workers"] = 0 # Run locally.
core_config["env_config"] = {"is_slippery": False, "map_name": "4x4"}
# Test against all frameworks.
for fw in framework_iterator(core_config):
config = core_config.copy()
# DQN with ParameterNoise exploration (config["explore"]=True).
# ----
config["exploration_config"] = {"type": "ParameterNoise"}
config["explore"] = True
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
policy = trainer.get_policy()
p_sess = getattr(policy, "_sess", None)
self.assertFalse(policy.exploration.weights_are_currently_noisy)
noise_before = self._get_current_noise(policy, fw)
check(noise_before, 0.0)
initial_weights = self._get_current_weight(policy, fw)
# Pseudo-start an episode and compare the weights before and after.
policy.exploration.on_episode_start(policy, tf_sess=p_sess)
self.assertFalse(policy.exploration.weights_are_currently_noisy)
noise_after_ep_start = self._get_current_noise(policy, fw)
weights_after_ep_start = self._get_current_weight(policy, fw)
# Should be the same, as we don't do anything at the beginning of
# the episode, only one step later.
check(noise_after_ep_start, noise_before)
check(initial_weights, weights_after_ep_start)
# Setting explore=False should always return the same action.
a_ = trainer.compute_action(obs, explore=False)
self.assertFalse(policy.exploration.weights_are_currently_noisy)
noise = self._get_current_noise(policy, fw)
# We sampled the first noise (not zero anymore).
check(noise, 0.0, false=True)
# But still not applied b/c explore=False.
check(self._get_current_weight(policy, fw), initial_weights)
for _ in range(10):
a = trainer.compute_action(obs, explore=False)
check(a, a_)
# Noise never gets applied.
check(self._get_current_weight(policy, fw), initial_weights)
self.assertFalse(
policy.exploration.weights_are_currently_noisy)
# Explore=None (default: True) should return different actions.
# However, this is only due to the underlying epsilon-greedy
# exploration.
actions = []
current_weight = None
for _ in range(10):
actions.append(trainer.compute_action(obs))
self.assertTrue(policy.exploration.weights_are_currently_noisy)
# Now, noise actually got applied (explore=True).
current_weight = self._get_current_weight(policy, fw)
check(current_weight, initial_weights, false=True)
check(current_weight, initial_weights + noise)
check(np.std(actions), 0.0, false=True)
# Pseudo-end the episode and compare weights again.
# Make sure they are the original ones.
policy.exploration.on_episode_end(policy, tf_sess=p_sess)
weights_after_ep_end = self._get_current_weight(policy, fw)
check(current_weight - noise, weights_after_ep_end, decimals=5)
# DQN with ParameterNoise exploration (config["explore"]=False).
# ----
config = core_config.copy()
config["exploration_config"] = {"type": "ParameterNoise"}
config["explore"] = False
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
policy = trainer.get_policy()
p_sess = getattr(policy, "_sess", None)
self.assertFalse(policy.exploration.weights_are_currently_noisy)
initial_weights = self._get_current_weight(policy, fw)
# Noise before anything (should be 0.0, no episode started yet).
noise = self._get_current_noise(policy, fw)
check(noise, 0.0)
# Pseudo-start an episode and compare the weights before and after
# (they should be the same).
policy.exploration.on_episode_start(policy, tf_sess=p_sess)
self.assertFalse(policy.exploration.weights_are_currently_noisy)
# Should be the same, as we don't do anything at the beginning of
# the episode, only one step later.
noise = self._get_current_noise(policy, fw)
check(noise, 0.0)
noisy_weights = self._get_current_weight(policy, fw)
check(initial_weights, noisy_weights)
# Setting explore=False or None should always return the same
# action.
a_ = trainer.compute_action(obs, explore=False)
# Now we have re-sampled.
noise = self._get_current_noise(policy, fw)
check(noise, 0.0, false=True)
for _ in range(5):
a = trainer.compute_action(obs, explore=None)
check(a, a_)
a = trainer.compute_action(obs, explore=False)
check(a, a_)
# Pseudo-end the episode and compare weights again.
# Make sure they are the original ones (no noise permanently
# applied throughout the episode).
policy.exploration.on_episode_end(policy, tf_sess=p_sess)
weights_after_episode_end = self._get_current_weight(policy, fw)
check(initial_weights, weights_after_episode_end)
# Noise should still be the same (re-sampling only happens at
# beginning of episode).
noise_after = self._get_current_noise(policy, fw)
check(noise, noise_after)
# Switch off EpsilonGreedy underlying exploration.
# ----
config = core_config.copy()
config["exploration_config"] = {
"type": "ParameterNoise",
"sub_exploration": {
"type": "EpsilonGreedy",
"action_space": trainer.get_policy().action_space,
"initial_epsilon": 0.0, # <- no randomness whatsoever
}
}
config["explore"] = True
trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
# Now, when we act - even with explore=True - we would expect
# the same action for the same input (parameter noise is
# deterministic).
policy = trainer.get_policy()
p_sess = getattr(policy, "_sess", None)
policy.exploration.on_episode_start(policy, tf_sess=p_sess)
a_ = trainer.compute_action(obs)
for _ in range(10):
a = trainer.compute_action(obs, explore=True)
check(a, a_)
def main():
args = parser.parse_args()
ray.init()
if args.agent not in ["DQN", "SlateQ"]:
raise ValueError(args.agent)
env_config = {
"slate_size": args.env_slate_size,
"seed": args.env_seed,
"convert_to_discrete_action_space": args.agent == "DQN",
}
if args.use_tune:
time_signature = datetime.now().strftime("%Y-%m-%d_%H_%M_%S")
name = f"SlateQ/{args.agent}-seed{args.env_seed}-{time_signature}"
if args.agent == "DQN":
tune.run(
"DQN",
stop={"timesteps_total": 4000000},
name=name,
config={
"env": recsim_env_name,
"num_gpus": args.num_gpus,
"num_workers": args.num_workers,
"env_config": env_config,
},
num_samples=args.tune_num_samples,
verbose=1,
)
else:
tune.run(
"SlateQ",
stop={"timesteps_total": 4000000},
name=name,
config={
"env": recsim_env_name,
"num_gpus": args.num_gpus,
"num_workers": args.num_workers,
"slateq_strategy": tune.grid_search(ALL_SLATEQ_STRATEGIES),
"env_config": env_config,
},
num_samples=args.tune_num_samples,
verbose=1,
)
else:
# directly run using the trainer interface (good for debugging)
if args.agent == "DQN":
config = dqn.DEFAULT_CONFIG.copy()
config["num_gpus"] = 0
config["num_workers"] = 0
config["env_config"] = env_config
trainer = dqn.DQNTrainer(config=config, env=recsim_env_name)
else:
config = slateq.DEFAULT_CONFIG.copy()
config["num_gpus"] = 0
config["num_workers"] = 0
config["slateq_strategy"] = args.strategy
config["env_config"] = env_config
trainer = slateq.SlateQTrainer(config=config, env=recsim_env_name)
for i in range(10):
result = trainer.train()
print(pretty_print(result))
ray.shutdown()
def main():
args = parser.parse_args()
ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
env_config = {
"num_candidates": args.env_num_candidates,
"resample_documents": not args.env_dont_resample_documents,
"slate_size": args.env_slate_size,
"seed": args.env_seed,
"convert_to_discrete_action_space": args.run == "DQN",
}
config = {
"env": (InterestEvolutionRecSimEnv if args.env == "interest-evolution"
else InterestExplorationRecSimEnv if args.env
== "interest-exploration" else LongTermSatisfactionRecSimEnv),
"framework":
args.framework,
"num_gpus":
args.num_gpus,
"num_workers":
args.num_workers,
"env_config":
env_config,
"learning_starts":
args.learning_starts,
}
# Perform a test run on the env with a random agent to see, what
# the random baseline reward is.
if args.random_test_episodes:
print(f"Running {args.random_test_episodes} episodes to get a random "
"agent's baseline reward ...")
env = config["env"](config=env_config)
env.reset()
num_episodes = 0
episode_rewards = []
episode_reward = 0.0
while num_episodes < args.random_test_episodes:
action = env.action_space.sample()
_, r, d, _ = env.step(action)
episode_reward += r
if d:
num_episodes += 1
episode_rewards.append(episode_reward)
episode_reward = 0.0
env.reset()
print(f"Ran {args.random_test_episodes} episodes with a random agent "
"reaching a mean episode return of "
f"{np.mean(episode_rewards)}+/-{sem(episode_rewards)}.")
if args.use_tune:
stop = {
"training_iteration": args.stop_iters,
"timesteps_total": args.stop_timesteps,
"episode_reward_mean": args.stop_reward,
}
if args.run == "SlateQ":
config.update({
"slateq_strategy": args.slateq_strategy,
})
results = tune.run(
args.run,
stop=stop,
config=config,
num_samples=args.tune_num_samples,
verbose=2,
)
if args.as_test:
check_learning_achieved(results, args.stop_reward)
else:
# Directly run using the trainer interface (good for debugging).
if args.run == "DQN":
trainer = dqn.DQNTrainer(config=config)
else:
config.update({
"slateq_strategy": args.slateq_strategy,
})
trainer = slateq.SlateQTrainer(config=config)
for i in range(10):
result = trainer.train()
print(pretty_print(result))
ray.shutdown()
mc = max(checkpoint_numbers)
checkpoint_path = path_to_results+"/"+"checkpoint_{}/checkpoint-{}".format(mc,mc)
print("found {} checkpoints".format(len(checkpoint_numbers)))
print("restoring "+checkpoint_path)
# ============================================================== #
# evaluation {{{
# ============================================================== #
#ray.init()
ray.init(temp_dir=tmpdir+"/ray") # you may need to change the temp directory in case it runs on a cluster or shared machine
if config["optimizer_class"] == "AsyncReplayOptimizer":
trainer = dqn.ApexTrainer(config=config, env=CodeEnv)
else:
trainer = dqn.DQNTrainer(config=config, env=CodeEnv)
trainer.restore(checkpoint_path)
env = CodeEnv(env_config)
n = env.n
dB_len = len(dB_range)
BitErr = np.zeros([dB_len], dtype=int)
CwErr = np.zeros([dB_len], dtype=int)
totCw = np.zeros([dB_len], dtype=int)
totBit = np.zeros([dB_len], dtype=int)
for i in range(dB_len):
print("\n--------\nSimulating EbNo = {} dB".format(dB_range[i]))
env.set_EbNo_dB(dB_range[i])
while(CwErr[i]<minCwErr and totCw[i]+1<=maxCw):