1cba589bd4
* Allow to specify trainer callbacks (train_fn, test_fn, stop_fn) in high-level API, adding the necessary abstractions and pass-on mechanisms * Add example atari_dqn_hl
28 lines
933 B
Python
28 lines
933 B
Python
from dataclasses import dataclass
|
|
|
|
|
|
@dataclass
|
|
class RLSamplingConfig:
|
|
"""Sampling, epochs, parallelization, buffers, collectors, and batching."""
|
|
|
|
# TODO: What are reasonable defaults?
|
|
num_epochs: int = 100
|
|
step_per_epoch: int = 30000
|
|
batch_size: int = 64
|
|
num_train_envs: int = 64
|
|
num_test_envs: int = 10
|
|
buffer_size: int = 4096
|
|
step_per_collect: int = 2048
|
|
repeat_per_collect: int | None = 10
|
|
update_per_step: float = 1.0
|
|
"""
|
|
Only used in off-policy algorithms.
|
|
How many gradient steps to perform per step in the environment (i.e., per sample added to the buffer).
|
|
"""
|
|
start_timesteps: int = 0
|
|
start_timesteps_random: bool = False
|
|
# TODO can we set the parameters below intelligently? Perhaps based on env. representation?
|
|
replay_buffer_ignore_obs_next: bool = False
|
|
replay_buffer_save_only_last_obs: bool = False
|
|
replay_buffer_stack_num: int = 1
|