def load_policy(env,
algo,
env_name,
policy_path=None,
coop=False,
seed=0,
extra_configs={}):
if algo == 'ppo':
agent = ppo.PPOTrainer(
setup_config(env, algo, coop, seed, extra_configs),
'assistive_gym:' + env_name)
elif algo == 'sac':
agent = sac.SACTrainer(
setup_config(env, algo, coop, seed, extra_configs),
'assistive_gym:' + env_name)
if policy_path != '':
if 'checkpoint' in policy_path:
agent.restore(policy_path)
else:
# Find the most recent policy in the directory
directory = os.path.join(policy_path, algo, env_name)
files = [
int(f.split('_')[-1])
for f in glob.glob(os.path.join(directory, 'checkpoint_*'))
]
if files:
checkpoint_num = max(files)
checkpoint_path = os.path.join(
directory, 'checkpoint_%d' % checkpoint_num,
'checkpoint-%d' % checkpoint_num)
agent.restore(checkpoint_path)
# return agent, checkpoint_path
return agent, None
return agent, None
def test_sac_compilation(self):
"""Tests whether an SACTrainer can be built with all frameworks."""
config = sac.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["twin_q"] = True
config["soft_horizon"] = True
config["clip_actions"] = False
config["normalize_actions"] = True
config["learning_starts"] = 0
config["prioritized_replay"] = True
num_iterations = 1
for _ in framework_iterator(config):
# Test for different env types (discrete w/ and w/o image, + cont).
for env in [
"Pendulum-v0", "MsPacmanNoFrameskip-v4", "CartPole-v0"
]:
print("Env={}".format(env))
config["use_state_preprocessor"] = \
env == "MsPacmanNoFrameskip-v4"
trainer = sac.SACTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
def test_sac_fake_multi_gpu_learning(self):
"""Test whether SACTrainer can learn CartPole w/ faked multi-GPU."""
config = copy.deepcopy(sac.DEFAULT_CONFIG)
# Fake GPU setup.
config["num_gpus"] = 2
config["_fake_gpus"] = True
config["clip_actions"] = False
config["initial_alpha"] = 0.001
config["prioritized_replay"] = True
env = "ray.rllib.examples.env.repeat_after_me_env.RepeatAfterMeEnv"
config["env_config"] = {"config": {"repeat_delay": 0}}
for _ in framework_iterator(config, frameworks=("tf", "torch")):
trainer = sac.SACTrainer(config=config, env=env)
num_iterations = 50
learnt = False
for i in range(num_iterations):
results = trainer.train()
print(f"R={results['episode_reward_mean']}")
if results["episode_reward_mean"] > 30.0:
learnt = True
break
assert learnt, \
f"SAC multi-GPU (with fake-GPUs) did not learn {env}!"
trainer.stop()
def get_PPO_trainer(use_gpu=1):
ModelCatalog.register_custom_model("my_model", TorchCustomModel)
config = {
"env": StoppingCar, #
"model": {
"custom_model": "my_model",
"fcnet_hiddens": [64, 64],
"fcnet_activation": "relu"
}, # model config,"
"lr": 5e-4,
"num_gpus": use_gpu,
# "vf_share_layers": False,
# "vf_clip_param": 100000,
"grad_clip": 2500,
"num_workers": 8, # parallelism
"batch_mode": "complete_episodes",
"evaluation_interval": 10,
# "use_gae": True, #
# "lambda": 0.95, # gae lambda param
"num_envs_per_worker": 10,
"train_batch_size": 4000,
"evaluation_num_episodes": 20,
"rollout_fragment_length": 1000,
"framework": "torch",
"horizon": 1000
}
# trainer = ppo.PPOTrainer(config=config)
trainer = sac.SACTrainer(config=config)
return config, trainer
def test_sac_compilation(self):
"""Tests whether an SACTrainer can be built with all frameworks."""
config = sac.DEFAULT_CONFIG.copy()
config["Q_model"] = sac.DEFAULT_CONFIG["Q_model"].copy()
config["num_workers"] = 0 # Run locally.
config["twin_q"] = True
config["clip_actions"] = False
config["normalize_actions"] = True
config["learning_starts"] = 0
config["prioritized_replay"] = True
config["rollout_fragment_length"] = 10
config["train_batch_size"] = 10
# If we use default buffer size (1e6), the buffer will take up
# 169.445 GB memory, which is beyond travis-ci's current (Mar 19, 2021)
# available system memory (8.34816 GB).
config["buffer_size"] = 40000
num_iterations = 1
ModelCatalog.register_custom_model("batch_norm", KerasBatchNormModel)
ModelCatalog.register_custom_model("batch_norm_torch",
TorchBatchNormModel)
image_space = Box(-1.0, 1.0, shape=(84, 84, 3))
simple_space = Box(-1.0, 1.0, shape=(3, ))
for fw in framework_iterator(config):
# Test for different env types (discrete w/ and w/o image, + cont).
for env in [
RandomEnv,
"MsPacmanNoFrameskip-v4",
"CartPole-v0",
]:
print("Env={}".format(env))
if env == RandomEnv:
config["env_config"] = {
"observation_space":
Tuple([simple_space,
Discrete(2), image_space]),
"action_space":
Box(-1.0, 1.0, shape=(1, )),
}
else:
config["env_config"] = {}
# Test making the Q-model a custom one for CartPole, otherwise,
# use the default model.
config["Q_model"]["custom_model"] = "batch_norm{}".format(
"_torch" if fw ==
"torch" else "") if env == "CartPole-v0" else None
trainer = sac.SACTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
def test_sac_dict_obs_order(self):
dict_space = Dict({
"img": Box(low=0, high=1, shape=(42, 42, 3)),
"cont": Box(low=0, high=100, shape=(3, )),
})
# Dict space .sample() returns an ordered dict.
# Make sure the keys in samples are ordered differently.
dict_samples = [{
k: v
for k, v in reversed(dict_space.sample().items())
} for _ in range(10)]
class NestedDictEnv(Env):
def __init__(self):
self.action_space = Box(low=-1.0, high=1.0, shape=(2, ))
self.observation_space = dict_space
self._spec = EnvSpec("NestedDictEnv-v0")
self.steps = 0
def reset(self):
self.steps = 0
return dict_samples[0]
def step(self, action):
self.steps += 1
return dict_samples[self.steps], 1, self.steps >= 5, {}
tune.register_env("nested", lambda _: NestedDictEnv())
config = sac.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["learning_starts"] = 0
config["rollout_fragment_length"] = 5
config["train_batch_size"] = 5
config["replay_buffer_config"]["capacity"] = 10
# Disable preprocessors.
config["_disable_preprocessor_api"] = True
num_iterations = 1
for _ in framework_iterator(config, with_eager_tracing=True):
trainer = sac.SACTrainer(env="nested", config=config)
for _ in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(trainer)
def test_sac_compilation(self):
"""Test whether an SACTrainer can be built with all frameworks."""
ray.init()
config = sac.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
num_iterations = 1
# eager (discrete and cont. actions).
for _ in framework_iterator(config, ["tf", "eager"]):
for env in [
"CartPole-v0",
"Pendulum-v0",
]:
print("Env={}".format(env))
trainer = sac.SACTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
print(results)
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 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 test_sac_loss_function(self):
"""Tests SAC loss function results across all frameworks."""
config = sac.DEFAULT_CONFIG.copy()
# Run locally.
config["num_workers"] = 0
config["learning_starts"] = 0
config["twin_q"] = False
config["gamma"] = 0.99
# Switch on deterministic loss so we can compare the loss values.
config["_deterministic_loss"] = True
# Use very simple nets.
config["Q_model"]["fcnet_hiddens"] = [10]
config["policy_model"]["fcnet_hiddens"] = [10]
# Make sure, timing differences do not affect trainer.train().
config["min_iter_time_s"] = 0
map_ = {
# Normal net.
"default_policy/sequential/action_1/kernel": "action_model."
"action_0._model.0.weight",
"default_policy/sequential/action_1/bias": "action_model."
"action_0._model.0.bias",
"default_policy/sequential/action_out/kernel": "action_model."
"action_out._model.0.weight",
"default_policy/sequential/action_out/bias": "action_model."
"action_out._model.0.bias",
"default_policy/sequential_1/q_hidden_0/kernel": "q_net."
"q_hidden_0._model.0.weight",
"default_policy/sequential_1/q_hidden_0/bias": "q_net."
"q_hidden_0._model.0.bias",
"default_policy/sequential_1/q_out/kernel": "q_net."
"q_out._model.0.weight",
"default_policy/sequential_1/q_out/bias": "q_net."
"q_out._model.0.bias",
"default_policy/value_out/kernel": "_value_branch."
"_model.0.weight",
"default_policy/value_out/bias": "_value_branch."
"_model.0.bias",
# Target net.
"default_policy/sequential_2/action_1/kernel": "action_model."
"action_0._model.0.weight",
"default_policy/sequential_2/action_1/bias": "action_model."
"action_0._model.0.bias",
"default_policy/sequential_2/action_out/kernel": "action_model."
"action_out._model.0.weight",
"default_policy/sequential_2/action_out/bias": "action_model."
"action_out._model.0.bias",
"default_policy/sequential_3/q_hidden_0/kernel": "q_net."
"q_hidden_0._model.0.weight",
"default_policy/sequential_3/q_hidden_0/bias": "q_net."
"q_hidden_0._model.0.bias",
"default_policy/sequential_3/q_out/kernel": "q_net."
"q_out._model.0.weight",
"default_policy/sequential_3/q_out/bias": "q_net."
"q_out._model.0.bias",
"default_policy/value_out_1/kernel": "_value_branch."
"_model.0.weight",
"default_policy/value_out_1/bias": "_value_branch."
"_model.0.bias",
}
env = SimpleEnv
batch_size = 100
if env is SimpleEnv:
obs_size = (batch_size, 1)
actions = np.random.random(size=(batch_size, 1))
elif env == "CartPole-v0":
obs_size = (batch_size, 4)
actions = np.random.randint(0, 2, size=(batch_size, ))
else:
obs_size = (batch_size, 3)
actions = np.random.random(size=(batch_size, 1))
# Batch of size=n.
input_ = self._get_batch_helper(obs_size, actions, batch_size)
# Simply compare loss values AND grads of all frameworks with each
# other.
prev_fw_loss = weights_dict = None
expect_c, expect_a, expect_e, expect_t = None, None, None, None
# History of tf-updated NN-weights over n training steps.
tf_updated_weights = []
# History of input batches used.
tf_inputs = []
for fw, sess in framework_iterator(config,
frameworks=("tf", "torch"),
session=True):
# Generate Trainer and get its default Policy object.
trainer = sac.SACTrainer(config=config, env=env)
policy = trainer.get_policy()
p_sess = None
if sess:
p_sess = policy.get_session()
# Set all weights (of all nets) to fixed values.
if weights_dict is None:
assert fw == "tf" # Start with the tf vars-dict.
weights_dict = policy.get_weights()
else:
assert fw == "torch" # Then transfer that to torch Model.
model_dict = self._translate_weights_to_torch(
weights_dict, map_)
policy.model.load_state_dict(model_dict)
policy.target_model.load_state_dict(model_dict)
if fw == "tf":
log_alpha = weights_dict["default_policy/log_alpha"]
elif fw == "torch":
# Actually convert to torch tensors.
input_ = policy._lazy_tensor_dict(input_)
input_ = {k: input_[k] for k in input_.keys()}
log_alpha = policy.model.log_alpha.detach().numpy()[0]
# Only run the expectation once, should be the same anyways
# for all frameworks.
if expect_c is None:
expect_c, expect_a, expect_e, expect_t = \
self._sac_loss_helper(input_, weights_dict,
sorted(weights_dict.keys()),
log_alpha, fw,
gamma=config["gamma"], sess=sess)
# Get actual outs and compare to expectation AND previous
# framework. c=critic, a=actor, e=entropy, t=td-error.
if fw == "tf":
c, a, e, t, tf_c_grads, tf_a_grads, tf_e_grads = \
p_sess.run([
policy.critic_loss,
policy.actor_loss,
policy.alpha_loss,
policy.td_error,
policy.optimizer().compute_gradients(
policy.critic_loss[0],
policy.model.q_variables()),
policy.optimizer().compute_gradients(
policy.actor_loss,
policy.model.policy_variables()),
policy.optimizer().compute_gradients(
policy.alpha_loss, policy.model.log_alpha)],
feed_dict=policy._get_loss_inputs_dict(
input_, shuffle=False))
tf_c_grads = [g for g, v in tf_c_grads]
tf_a_grads = [g for g, v in tf_a_grads]
tf_e_grads = [g for g, v in tf_e_grads]
elif fw == "torch":
loss_torch(policy, policy.model, None, input_)
c, a, e, t = policy.critic_loss, policy.actor_loss, \
policy.alpha_loss, policy.td_error
# Test actor gradients.
policy.actor_optim.zero_grad()
assert all(v.grad is None for v in policy.model.q_variables())
assert all(v.grad is None
for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
a.backward()
# `actor_loss` depends on Q-net vars (but these grads must
# be ignored and overridden in critic_loss.backward!).
assert not any(v.grad is None
for v in policy.model.q_variables())
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.policy_variables())
assert not all(
torch.min(v.grad) == 0
for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
]
for tf_g, torch_g in zip(tf_a_grads, torch_a_grads):
if tf_g.shape != torch_g.shape:
check(tf_g, np.transpose(torch_g))
else:
check(tf_g, torch_g)
# Test critic gradients.
policy.critic_optims[0].zero_grad()
assert all(
torch.mean(v.grad) == 0.0
for v in policy.model.q_variables())
assert all(
torch.min(v.grad) == 0.0
for v in policy.model.q_variables())
assert policy.model.log_alpha.grad is None
c[0].backward()
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.q_variables())
assert not all(
torch.min(v.grad) == 0 for v in policy.model.q_variables())
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_c_grads = [v.grad for v in policy.model.q_variables()]
for tf_g, torch_g in zip(tf_c_grads, torch_c_grads):
if tf_g.shape != torch_g.shape:
check(tf_g, np.transpose(torch_g))
else:
check(tf_g, torch_g)
# Compare (unchanged(!) actor grads) with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
]
for tf_g, torch_g in zip(tf_a_grads, torch_a_grads):
if tf_g.shape != torch_g.shape:
check(tf_g, np.transpose(torch_g))
else:
check(tf_g, torch_g)
# Test alpha gradient.
policy.alpha_optim.zero_grad()
assert policy.model.log_alpha.grad is None
e.backward()
assert policy.model.log_alpha.grad is not None
check(policy.model.log_alpha.grad, tf_e_grads)
check(c, expect_c)
check(a, expect_a)
check(e, expect_e)
check(t, expect_t)
# Store this framework's losses in prev_fw_loss to compare with
# next framework's outputs.
if prev_fw_loss is not None:
check(c, prev_fw_loss[0])
check(a, prev_fw_loss[1])
check(e, prev_fw_loss[2])
check(t, prev_fw_loss[3])
prev_fw_loss = (c, a, e, t)
# Update weights from our batch (n times).
for update_iteration in range(10):
print("train iteration {}".format(update_iteration))
if fw == "tf":
in_ = self._get_batch_helper(obs_size, actions, batch_size)
tf_inputs.append(in_)
# Set a fake-batch to use
# (instead of sampling from replay buffer).
trainer.optimizer._fake_batch = in_
trainer.train()
updated_weights = policy.get_weights()
# Net must have changed.
if tf_updated_weights:
check(updated_weights[
"default_policy/sequential/action_1/kernel"],
tf_updated_weights[-1]
["default_policy/sequential/action_1/kernel"],
false=True)
tf_updated_weights.append(updated_weights)
# Compare with updated tf-weights. Must all be the same.
else:
tf_weights = tf_updated_weights[update_iteration]
in_ = tf_inputs[update_iteration]
# Set a fake-batch to use
# (instead of sampling from replay buffer).
trainer.optimizer._fake_batch = in_
trainer.train()
# Compare updated model.
for tf_key in sorted(tf_weights.keys())[2:10]:
tf_var = tf_weights[tf_key]
torch_var = policy.model.state_dict()[map_[tf_key]]
if tf_var.shape != torch_var.shape:
check(tf_var, np.transpose(torch_var), rtol=0.05)
else:
check(tf_var, torch_var, rtol=0.05)
# And alpha.
check(policy.model.log_alpha,
tf_weights["default_policy/log_alpha"])
# Compare target nets.
for tf_key in sorted(tf_weights.keys())[10:18]:
tf_var = tf_weights[tf_key]
torch_var = policy.target_model.state_dict()[
map_[tf_key]]
if tf_var.shape != torch_var.shape:
check(tf_var, np.transpose(torch_var), rtol=0.05)
else:
check(tf_var, torch_var, rtol=0.05)
def test_sac_loss_function(self):
"""Tests SAC loss function results across all frameworks."""
config = sac.DEFAULT_CONFIG.copy()
# Run locally.
config["num_workers"] = 0
config["learning_starts"] = 0
config["twin_q"] = False
config["gamma"] = 0.99
# Switch on deterministic loss so we can compare the loss values.
config["_deterministic_loss"] = True
# Use very simple nets.
config["Q_model"]["fcnet_hiddens"] = [10]
config["policy_model"]["fcnet_hiddens"] = [10]
# Make sure, timing differences do not affect trainer.train().
config["min_iter_time_s"] = 0
# Test SAC with Simplex action space.
config["env_config"] = {"simplex_actions": True}
map_ = {
# Action net.
"default_policy/fc_1/kernel": "action_model._hidden_layers.0."
"_model.0.weight",
"default_policy/fc_1/bias": "action_model._hidden_layers.0."
"_model.0.bias",
"default_policy/fc_out/kernel": "action_model."
"_logits._model.0.weight",
"default_policy/fc_out/bias": "action_model._logits._model.0.bias",
"default_policy/value_out/kernel": "action_model."
"_value_branch._model.0.weight",
"default_policy/value_out/bias": "action_model."
"_value_branch._model.0.bias",
# Q-net.
"default_policy/fc_1_1/kernel": "q_net."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_1/bias": "q_net."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_1/kernel": "q_net._logits._model.0.weight",
"default_policy/fc_out_1/bias": "q_net._logits._model.0.bias",
"default_policy/value_out_1/kernel": "q_net."
"_value_branch._model.0.weight",
"default_policy/value_out_1/bias": "q_net."
"_value_branch._model.0.bias",
"default_policy/log_alpha": "log_alpha",
# Target action-net.
"default_policy/fc_1_2/kernel": "action_model."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_2/bias": "action_model."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_2/kernel": "action_model."
"_logits._model.0.weight",
"default_policy/fc_out_2/bias": "action_model."
"_logits._model.0.bias",
"default_policy/value_out_2/kernel": "action_model."
"_value_branch._model.0.weight",
"default_policy/value_out_2/bias": "action_model."
"_value_branch._model.0.bias",
# Target Q-net
"default_policy/fc_1_3/kernel": "q_net."
"_hidden_layers.0._model.0.weight",
"default_policy/fc_1_3/bias": "q_net."
"_hidden_layers.0._model.0.bias",
"default_policy/fc_out_3/kernel": "q_net."
"_logits._model.0.weight",
"default_policy/fc_out_3/bias": "q_net."
"_logits._model.0.bias",
"default_policy/value_out_3/kernel": "q_net."
"_value_branch._model.0.weight",
"default_policy/value_out_3/bias": "q_net."
"_value_branch._model.0.bias",
"default_policy/log_alpha_1": "log_alpha",
}
env = SimpleEnv
batch_size = 100
obs_size = (batch_size, 1)
actions = np.random.random(size=(batch_size, 2))
# Batch of size=n.
input_ = self._get_batch_helper(obs_size, actions, batch_size)
# Simply compare loss values AND grads of all frameworks with each
# other.
prev_fw_loss = weights_dict = None
expect_c, expect_a, expect_e, expect_t = None, None, None, None
# History of tf-updated NN-weights over n training steps.
tf_updated_weights = []
# History of input batches used.
tf_inputs = []
for fw, sess in framework_iterator(config,
frameworks=("tf", "torch"),
session=True):
# Generate Trainer and get its default Policy object.
trainer = sac.SACTrainer(config=config, env=env)
policy = trainer.get_policy()
p_sess = None
if sess:
p_sess = policy.get_session()
# Set all weights (of all nets) to fixed values.
if weights_dict is None:
# Start with the tf vars-dict.
assert fw in ["tf2", "tf", "tfe"]
weights_dict = policy.get_weights()
if fw == "tfe":
log_alpha = weights_dict[10]
weights_dict = self._translate_tfe_weights(
weights_dict, map_)
else:
assert fw == "torch" # Then transfer that to torch Model.
model_dict = self._translate_weights_to_torch(
weights_dict, map_)
# Have to add this here (not a parameter in tf, but must be
# one in torch, so it gets properly copied to the GPU(s)).
model_dict["target_entropy"] = policy.model.target_entropy
policy.model.load_state_dict(model_dict)
policy.target_model.load_state_dict(model_dict)
if fw == "tf":
log_alpha = weights_dict["default_policy/log_alpha"]
elif fw == "torch":
# Actually convert to torch tensors (by accessing everything).
input_ = policy._lazy_tensor_dict(input_)
input_ = {k: input_[k] for k in input_.keys()}
log_alpha = policy.model.log_alpha.detach().cpu().numpy()[0]
# Only run the expectation once, should be the same anyways
# for all frameworks.
if expect_c is None:
expect_c, expect_a, expect_e, expect_t = \
self._sac_loss_helper(input_, weights_dict,
sorted(weights_dict.keys()),
log_alpha, fw,
gamma=config["gamma"], sess=sess)
# Get actual outs and compare to expectation AND previous
# framework. c=critic, a=actor, e=entropy, t=td-error.
if fw == "tf":
c, a, e, t, tf_c_grads, tf_a_grads, tf_e_grads = \
p_sess.run([
policy.critic_loss,
policy.actor_loss,
policy.alpha_loss,
policy.td_error,
policy.optimizer().compute_gradients(
policy.critic_loss[0],
[v for v in policy.model.q_variables() if
"value_" not in v.name]),
policy.optimizer().compute_gradients(
policy.actor_loss,
[v for v in policy.model.policy_variables() if
"value_" not in v.name]),
policy.optimizer().compute_gradients(
policy.alpha_loss, policy.model.log_alpha)],
feed_dict=policy._get_loss_inputs_dict(
input_, shuffle=False))
tf_c_grads = [g for g, v in tf_c_grads]
tf_a_grads = [g for g, v in tf_a_grads]
tf_e_grads = [g for g, v in tf_e_grads]
elif fw == "tfe":
with tf.GradientTape() as tape:
tf_loss(policy, policy.model, None, input_)
c, a, e, t = policy.critic_loss, policy.actor_loss, \
policy.alpha_loss, policy.td_error
vars = tape.watched_variables()
tf_c_grads = tape.gradient(c[0], vars[6:10])
tf_a_grads = tape.gradient(a, vars[2:6])
tf_e_grads = tape.gradient(e, vars[10])
elif fw == "torch":
loss_torch(policy, policy.model, None, input_)
c, a, e, t = policy.critic_loss, policy.actor_loss, \
policy.alpha_loss, policy.model.td_error
# Test actor gradients.
policy.actor_optim.zero_grad()
assert all(v.grad is None for v in policy.model.q_variables())
assert all(v.grad is None
for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
a.backward()
# `actor_loss` depends on Q-net vars (but these grads must
# be ignored and overridden in critic_loss.backward!).
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.policy_variables())
assert not all(
torch.min(v.grad) == 0
for v in policy.model.policy_variables())
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
if v.grad is not None
]
check(tf_a_grads[2],
np.transpose(torch_a_grads[0].detach().cpu()))
# Test critic gradients.
policy.critic_optims[0].zero_grad()
assert all(
torch.mean(v.grad) == 0.0
for v in policy.model.q_variables() if v.grad is not None)
assert all(
torch.min(v.grad) == 0.0
for v in policy.model.q_variables() if v.grad is not None)
assert policy.model.log_alpha.grad is None
c[0].backward()
assert not all(
torch.mean(v.grad) == 0
for v in policy.model.q_variables() if v.grad is not None)
assert not all(
torch.min(v.grad) == 0
for v in policy.model.q_variables() if v.grad is not None)
assert policy.model.log_alpha.grad is None
# Compare with tf ones.
torch_c_grads = [v.grad for v in policy.model.q_variables()]
check(tf_c_grads[0],
np.transpose(torch_c_grads[2].detach().cpu()))
# Compare (unchanged(!) actor grads) with tf ones.
torch_a_grads = [
v.grad for v in policy.model.policy_variables()
]
check(tf_a_grads[2],
np.transpose(torch_a_grads[0].detach().cpu()))
# Test alpha gradient.
policy.alpha_optim.zero_grad()
assert policy.model.log_alpha.grad is None
e.backward()
assert policy.model.log_alpha.grad is not None
check(policy.model.log_alpha.grad, tf_e_grads)
check(c, expect_c)
check(a, expect_a)
check(e, expect_e)
check(t, expect_t)
# Store this framework's losses in prev_fw_loss to compare with
# next framework's outputs.
if prev_fw_loss is not None:
check(c, prev_fw_loss[0])
check(a, prev_fw_loss[1])
check(e, prev_fw_loss[2])
check(t, prev_fw_loss[3])
prev_fw_loss = (c, a, e, t)
# Update weights from our batch (n times).
for update_iteration in range(5):
print("train iteration {}".format(update_iteration))
if fw == "tf":
in_ = self._get_batch_helper(obs_size, actions, batch_size)
tf_inputs.append(in_)
# Set a fake-batch to use
# (instead of sampling from replay buffer).
buf = LocalReplayBuffer.get_instance_for_testing()
buf._fake_batch = in_
trainer.train()
updated_weights = policy.get_weights()
# Net must have changed.
if tf_updated_weights:
check(updated_weights["default_policy/fc_1/kernel"],
tf_updated_weights[-1]
["default_policy/fc_1/kernel"],
false=True)
tf_updated_weights.append(updated_weights)
# Compare with updated tf-weights. Must all be the same.
else:
tf_weights = tf_updated_weights[update_iteration]
in_ = tf_inputs[update_iteration]
# Set a fake-batch to use
# (instead of sampling from replay buffer).
buf = LocalReplayBuffer.get_instance_for_testing()
buf._fake_batch = in_
trainer.train()
# Compare updated model.
for tf_key in sorted(tf_weights.keys()):
if re.search("_[23]|alpha", tf_key):
continue
tf_var = tf_weights[tf_key]
torch_var = policy.model.state_dict()[map_[tf_key]]
if tf_var.shape != torch_var.shape:
check(tf_var,
np.transpose(torch_var.detach().cpu()),
atol=0.003)
else:
check(tf_var, torch_var, atol=0.003)
# And alpha.
check(policy.model.log_alpha,
tf_weights["default_policy/log_alpha"])
# Compare target nets.
for tf_key in sorted(tf_weights.keys()):
if not re.search("_[23]", tf_key):
continue
tf_var = tf_weights[tf_key]
torch_var = policy.target_model.state_dict()[
map_[tf_key]]
if tf_var.shape != torch_var.shape:
check(tf_var,
np.transpose(torch_var.detach().cpu()),
atol=0.003)
else:
check(tf_var, torch_var, atol=0.003)
trainer.stop()
if render:
env.render()
if done == 1 and reward > 0:
break
if __name__ == "__main__":
ray.shutdown()
ray.init(ignore_reinit_error=True)
config = sac.DEFAULT_CONFIG.copy()
config["log_level"] = "WARN"
register_env("projectile-v0", lambda config: Projectile_v0())
# train a policy with RLlib using SAC
agent = sac.SACTrainer(config, env=SELECT_ENV)
checkpoint_path, reward_history = train_policy(agent, CHECKPOINT_PATH)
print(reward_history)
# apply the trained policy in a use case
agent.restore(checkpoint_path)
env = gym.make(SELECT_ENV)
rollout_actions(agent, env)
config['evaluation_config'] = {'explore': False}
config['evaluation_num_workers'] = 0 # 0 = Berechnung auf worker für training
# [Fortgeschrittene Einstellungen]
config[
'num_gpus'] = 0 # Anzahl der GPUs bei der Berechnung, 0 = keine GPU-Berechnung
config['framework'] = 'tf2' # tf, tfe, tf2 verfügbar
config['eager_tracing'] = True
config['rollout_fragment_length'] = 1
config['train_batch_size'] = 1
config['explore'] = False
config['normalize_actions'] = True
# [Verwenden vorheriger Daten]
config['input'] = os.path.join(os.path.dirname(__file__),
'Output_Data_{}'.format(trainer))
config['input_evaluation'] = []
ray.init()
if trainer == 'PPO':
from ray.rllib.agents import ppo
agent = ppo.PPOTrainer(env=dummyenv, config=config)
elif trainer == 'SAC':
from ray.rllib.agents import sac
agent = sac.SACTrainer(env=dummyenv, config=config)
for n in range(10):
result = agent.train()
for el in self.interfaces:
ray.get(el.clear_callback.remote())
time.sleep(INTERVAL)
self.end_episode(eid, obs)
register_env("moody", lambda _: MoodyEnvLoop(interfaces, my_config, INTERVAL))
config = sac.DEFAULT_CONFIG.copy()
config["num_gpus"] = 0
config["num_workers"] = 0
config["eager"] = False
config["timesteps_per_iteration"] = 20
config["learning_starts"] = 60
config['use_state_preprocessor'] = True
# Required to prevent rllib from thinking we subclass gym env
config["normalize_actions"] = False
trainer = sac.SACTrainer(config=config, env="moody")
print("Beginning training.")
for i in range(0, 100):
print(i)
result = trainer.train()
print("Iteration {}, reward {}, timesteps {}".format(
i, result["episode_reward_mean"], result["timesteps_total"]))
trainer.save()
temp_env = Nav2dEnv()
action_space = temp_env.action_space
observation_space = temp_env.observation_space
print("ACTION SPACE : ", action_space, "OBSERVATION SPACE : ",
observation_space)
#### Config ##################################################
config = {
"framework": "tf",
"env": temp_env_name,
"num_workers": 1,
}
#### Restore agent #########################################
agent = sac.SACTrainer(config=config)
checkpoint = r"C:\Users\Arcn\Desktop\twodenv\single\results\checkpoint_950\checkpoint-950"
agent.restore(checkpoint)
#### Create test environment ###############################
test_env = Nav2dEnv()
for ep in range(10):
#### Show, record a video, and log the model's performance #
obs = test_env.reset()
action = action_space.sample()
start = time.time()
policy = agent.get_policy()
def test_sac_compilation(self):
"""Tests whether an SACTrainer can be built with all frameworks."""
config = sac.DEFAULT_CONFIG.copy()
config["Q_model"] = sac.DEFAULT_CONFIG["Q_model"].copy()
config["num_workers"] = 0 # Run locally.
config["n_step"] = 3
config["twin_q"] = True
config["learning_starts"] = 0
config["prioritized_replay"] = True
config["rollout_fragment_length"] = 10
config["train_batch_size"] = 10
# If we use default buffer size (1e6), the buffer will take up
# 169.445 GB memory, which is beyond travis-ci's current (Mar 19, 2021)
# available system memory (8.34816 GB).
config["buffer_size"] = 40000
# Test with saved replay buffer.
config["store_buffer_in_checkpoints"] = True
num_iterations = 1
ModelCatalog.register_custom_model("batch_norm", KerasBatchNormModel)
ModelCatalog.register_custom_model("batch_norm_torch",
TorchBatchNormModel)
image_space = Box(-1.0, 1.0, shape=(84, 84, 3))
simple_space = Box(-1.0, 1.0, shape=(3, ))
tune.register_env(
"random_dict_env",
lambda _: RandomEnv({
"observation_space":
Dict({
"a": simple_space,
"b": Discrete(2),
"c": image_space,
}),
"action_space":
Box(-1.0, 1.0, shape=(1, )),
}),
)
tune.register_env(
"random_tuple_env",
lambda _: RandomEnv({
"observation_space":
Tuple([simple_space, Discrete(2), image_space]),
"action_space":
Box(-1.0, 1.0, shape=(1, )),
}),
)
for fw in framework_iterator(config, with_eager_tracing=True):
# Test for different env types (discrete w/ and w/o image, + cont).
for env in [
"random_dict_env",
"random_tuple_env",
# "MsPacmanNoFrameskip-v4",
"CartPole-v0",
]:
print("Env={}".format(env))
# Test making the Q-model a custom one for CartPole, otherwise,
# use the default model.
config["Q_model"]["custom_model"] = (
"batch_norm{}".format("_torch" if fw == "torch" else "")
if env == "CartPole-v0" else None)
trainer = sac.SACTrainer(config=config, env=env)
for i in range(num_iterations):
results = trainer.train()
check_train_results(results)
print(results)
check_compute_single_action(trainer)
# Test, whether the replay buffer is saved along with
# a checkpoint (no point in doing it for all frameworks since
# this is framework agnostic).
if fw == "tf" and env == "CartPole-v0":
checkpoint = trainer.save()
new_trainer = sac.SACTrainer(config, env=env)
new_trainer.restore(checkpoint)
# Get some data from the buffer and compare.
data = trainer.local_replay_buffer.replay_buffers[
"default_policy"]._storage[:42 + 42]
new_data = new_trainer.local_replay_buffer.replay_buffers[
"default_policy"]._storage[:42 + 42]
check(data, new_data)
new_trainer.stop()
trainer.stop()
def render(checkpoint, home_path):
"""
Renders pybullet and mujoco environments.
"""
alg = re.match('.+?(?=_)', os.path.basename(os.path.normpath(home_path))).group(0)
current_env = re.search("(?<=_).*?(?=_)", os.path.basename(os.path.normpath(home_path))).group(0)
checkpoint_path = home_path + "checkpoint_" + str(checkpoint) + "/checkpoint-" + str(checkpoint)
config = json.load(open(home_path + "params.json"))
config_bin = pickle.load(open(home_path + "params.pkl", "rb"))
ray.shutdown()
import pybullet_envs
ray.init()
ModelCatalog.register_custom_model("RBF", RBFModel)
ModelCatalog.register_custom_model("MLP_2_64", MLP)
ModelCatalog.register_custom_model("linear", Linear)
if alg == "PPO":
trainer = ppo.PPOTrainer(config_bin)
if alg == "SAC":
trainer = sac.SACTrainer(config)
if alg == "DDPG":
trainer = ddpg.DDPGTrainer(config)
if alg == "PG":
trainer = pg.PGTrainer(config)
if alg == "A3C":
trainer = a3c.A3CTrainer(config)
if alg == "TD3":
trainer = td3.TD3Trainer(config)
if alg == "ES":
trainer = es.ESTrainer(config)
if alg == "ARS":
trainer = ars.ARSTrainer(config)
# "normalize_actions": true,
trainer.restore(checkpoint_path)
if "Bullet" in current_env:
env = gym.make(current_env, render=True)
else:
env = gym.make(current_env)
#env.unwrapped.reset_model = det_reset_model
env._max_episode_steps = 10000
obs = env.reset()
action_hist = []
m_act_hist = []
state_hist = []
obs_hist = []
reward_hist = []
done = False
step = 0
for t in range(10000):
# for some algorithms you can get the sample mean out, need to change the value on the index to match your env for now
# mean_actions = out_dict['behaviour_logits'][:17]
# actions = trainer.compute_action(obs.flatten())
# sampled_actions, _ , out_dict = trainer.compute_action(obs.flatten(),full_fetch=True)
sampled_actions = trainer.compute_action(obs.flatten())
# sampled_actions, _ , out_dict = trainer.compute_action(obs.flatten(),full_fetch=True)
actions = sampled_actions
obs, reward, done, _ = env.step(np.asarray(actions))
# env.camera_adjust()
env.render(mode='human')
time.sleep(0.01)
# env.render()
# env.render(mode='rgb_array', close = True)
# p.computeViewMatrix(cameraEyePosition=[0,10,5], cameraTargetPosition=[0,0,0], cameraUpVector=[0,0,0])
# if step % 1000 == 0:
# env.reset()
# step += 1
action_hist.append(np.copy(actions))
obs_hist.append(np.copy(obs))
reward_hist.append(np.copy(reward))
if done:
obs = env.reset()
# print(sum(reward_hist))
# print((obs_hist))
#plt.plot(action_hist)
#plt.figure()
#plt.figure()
#plt.plot(obs_hist)
#plt.figure()
# Reminder that the bahavior logits that come out are the mean and logstd (not log mean, despite the name logit)
# trainer.compute_action(obs, full_fetch=True)
trainer.compute_action(obs)
config = sac.DEFAULT_CONFIG.copy()
# Add a new RE3UpdateCallbacks
config["callbacks"] = MultiCallbacks([
config["callbacks"],
partial(
RE3UpdateCallbacks,
embeds_dim=128,
beta_schedule="linear_decay",
k_nn=50,
),
])
config["env"] = "LunarLanderContinuous-v2"
config["seed"] = 12345
# Add type as RE3 in the exploration_config parameter
config["exploration_config"] = {
"type": "RE3",
"sub_exploration": {
"type": "StochasticSampling",
},
}
num_iterations = 2000
trainer = sac.SACTrainer(config=config)
for i in range(num_iterations):
result = trainer.train()
print(result)
trainer.stop()
ray.shutdown()
trial=analysis.get_best_trial("episode_reward_mean"),
metric="episode_reward_mean")
print('checkpoints=', checkpoints)
checkpoint_path, reward = checkpoints[0]
print('checkpoint_path=', checkpoint_path)
config = {
"env": env_name,
"num_gpus": 0,
"num_workers": 1,
"framework": "tf2",
}
#agent = ppo.PPOTrainer(config=config, env=env_name)
agent = sac.SACTrainer(config=config, env=env_name)
agent.restore(checkpoint_path)
print('agent=', agent)
########################################
import gym
# instantiate env class
env = gym.make(env_name)
# run until episode ends
episode_reward = 0
done = False
obs = env.reset()
while not done:
ModelCatalog.register_custom_model("MLPModel", MLPModel)
ModelCatalog.register_custom_model("MLPModelV2", MLPModelV2)
if algorithm == 'A2C':
RLAgent = a2c.A2CTrainer(env=env_name, config=config)
elif algorithm == 'APEX_DDPG':
RLAgent = apex.ApexDDPGTrainer(env=env_name, config=config)
elif algorithm == 'DDPG':
RLAgent = ddpg.DDPGTrainer(env=env_name, config=config)
elif algorithm == 'IMPALA':
RLAgent = impala.ImpalaTrainer(env=env_name, config=config)
elif algorithm == 'PPO':
RLAgent = ppo.PPOTrainer(env=env_name, config=config)
elif algorithm == 'SAC':
RLAgent = sac.SACTrainer(env=env_name, config=config)
elif algorithm == 'TD3':
RLAgent = td3.TD3Trainer(env=env_name, config=config)
RLAgent.restore(checkpoint_path)
num_runs = 50
totalRewards = np.empty((num_runs, ))
for j in range(num_runs):
observations = env.reset()
rewards, action_dict = {}, {}
for agent_id in env.agent_ids:
assert isinstance(agent_id, int), "Error: agent_ids are not ints."
action_dict = dict(
zip(env.agent_ids,
[env.action_space_dict[i].sample() for i in env.agent_ids]))
