total_timesteps += len(batch)
# Add NEXT_OBS if not available. This is slightly hacked
# as for the very last time step, we will use next-obs=zeros
# and therefore force-set DONE=True to avoid this missing
# next-obs to cause learning problems.
if SampleBatch.NEXT_OBS not in batch:
obs = batch[SampleBatch.OBS]
batch[SampleBatch.NEXT_OBS] = \
np.concatenate([obs[1:], np.zeros_like(obs[0:1])])
batch[SampleBatch.DONES][-1] = True
replay_buffer.add_batch(batch)
print(
f"Loaded {num_batches} batches ({total_timesteps} ts) into the "
f"replay buffer, which has capacity {replay_buffer.buffer_size}.")
else:
raise ValueError(
"Unknown offline input! config['input'] must either be list of "
"offline files (json) or a D4RL-specific InputReader specifier "
"(e.g. 'd4rl.hopper-medium-v0').")
CQLTrainer = SACTrainer.with_updates(
name="CQL",
default_config=CQL_DEFAULT_CONFIG,
validate_config=validate_config,
default_policy=CQLTFPolicy,
get_policy_class=get_policy_class,
after_init=after_init,
execution_plan=execution_plan,
)
from ray.rllib.agents.sac.sac import SACTrainer
from ray.rllib.agents.sac.sac import DEFAULT_CONFIG as SAC_CONFIG
from ray.rllib.utils import merge_dicts
APEX_SAC_DEFAULT_CONFIG = merge_dicts(
SAC_CONFIG, # see also the options in ddpg.py, which are also supported
{
"optimizer": merge_dicts(
SAC_CONFIG["optimizer"], {
"max_weight_sync_delay": 400,
"num_replay_buffer_shards": 4,
"debug": False
}),
"num_gpus": 0,
"num_workers": 32,
"buffer_size": 2000000,
"learning_starts": 50000,
"train_batch_size": 512,
"sample_batch_size": 50,
"target_network_update_freq": 500000,
"timesteps_per_iteration": 25000,
"per_worker_exploration": True,
"worker_side_prioritization": True,
"min_iter_time_s": 30,
},
)
ApexSACTrainer = SACTrainer.with_updates(
name="APEX_SAC",
default_config=APEX_SAC_DEFAULT_CONFIG,
**APEX_TRAINER_PROPERTIES)
# Num Actions to sample for CQL Loss
"num_actions": 10,
# Whether to use the Langrangian for Alpha Prime (in CQL Loss)
"lagrangian": False,
# Lagrangian Threshold
"lagrangian_thresh": 5.0,
# Min Q Weight multiplier
"min_q_weight": 5.0,
})
# __sphinx_doc_end__
# yapf: enable
def validate_config(config: TrainerConfigDict):
if config["framework"] == "tf":
raise ValueError("Tensorflow CQL not implemented yet!")
def get_policy_class(config: TrainerConfigDict) -> Optional[Type[Policy]]:
if config["framework"] == "torch":
return CQLTorchPolicy
CQLTrainer = SACTrainer.with_updates(
name="CQL",
default_config=CQL_DEFAULT_CONFIG,
validate_config=validate_config,
default_policy=CQLTorchPolicy,
get_policy_class=get_policy_class,
)
def make_policy_optimizer(workers, config):
"""Create the single process DQN policy optimizer.
Returns:
SyncReplayOptimizer: Used for generic off-policy Trainers.
"""
return SyncReplayOptimizerModified(
workers,
learning_starts=config["learning_starts"],
buffer_size=config["buffer_size"],
prioritized_replay=config["prioritized_replay"],
prioritized_replay_alpha=config["prioritized_replay_alpha"],
prioritized_replay_beta=config["prioritized_replay_beta"],
prioritized_replay_beta_annealing_timesteps=config[
"prioritized_replay_beta_annealing_timesteps"],
final_prioritized_replay_beta=config["final_prioritized_replay_beta"],
prioritized_replay_eps=config["prioritized_replay_eps"],
train_batch_size=config["train_batch_size"],
**config["optimizer"])
DiCESACTrainer = SACTrainer.with_updates(
name="DiCESACTrainer",
default_config=dice_sac_default_config,
default_policy=DiCESACPolicy,
get_policy_class=lambda _: DiCESACPolicy,
after_init=setup_policies_pool,
after_optimizer_step=after_optimizer_step,
validate_config=validate_config,
make_policy_optimizer=make_policy_optimizer)