from ding.utils import POLICY_REGISTRY
from .ddpg import DDPGPolicy
[docs]@POLICY_REGISTRY.register('td3')
class TD3Policy(DDPGPolicy):
r"""
Overview:
Policy class of TD3 algorithm. Since DDPG and TD3 share many common things, we can easily derive this TD3
class from DDPG class by changing ``_actor_update_freq``, ``_twin_critic`` and noise in model wrapper.
Property:
learn_mode, collect_mode, eval_mode
Config:
== ==================== ======== ================== ================================= =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ================== ================================= =======================
1 ``type`` str td3 | RL policy register name, refer | this arg is optional,
| to registry ``POLICY_REGISTRY`` | a placeholder
2 ``cuda`` bool True | Whether to use cuda for network |
3 | ``random_`` int 25000 | Number of randomly collected | Default to 25000 for
| ``collect_size`` | training samples in replay | DDPG/TD3, 10000 for
| | buffer when training starts. | sac.
4 | ``model.twin_`` bool True | Whether to use two critic | Default True for TD3,
| ``critic`` | networks or only one. | Clipped Double
| | | Q-learning method in
| | | TD3 paper.
5 | ``learn.learning`` float 1e-3 | Learning rate for actor |
| ``_rate_actor`` | network(aka. policy). |
6 | ``learn.learning`` float 1e-3 | Learning rates for critic |
| ``_rate_critic`` | network (aka. Q-network). |
7 | ``learn.actor_`` int 2 | When critic network updates | Default 2 for TD3, 1
| ``update_freq`` | once, how many times will actor | for DDPG. Delayed
| | network update. | Policy Updates method
| | | in TD3 paper.
8 | ``learn.noise`` bool True | Whether to add noise on target | Default True for TD3,
| | network's action. | False for DDPG.
| | | Target Policy Smoo-
| | | thing Regularization
| | | in TD3 paper.
9 | ``learn.noise_`` dict | dict(min=-0.5, | Limit for range of target |
| ``range`` | max=0.5,) | policy smoothing noise, |
| | | aka. noise_clip. |
10 | ``learn.-`` bool False | Determine whether to ignore | Use ignore_done only
| ``ignore_done`` | done flag. | in halfcheetah env.
11 | ``learn.-`` float 0.005 | Used for soft update of the | aka. Interpolation
| ``target_theta`` | target network. | factor in polyak aver
| | | aging for target
| | | networks.
12 | ``collect.-`` float 0.1 | Used for add noise during co- | Sample noise from dis
| ``noise_sigma`` | llection, through controlling | tribution, Ornstein-
| | the sigma of distribution | Uhlenbeck process in
| | | DDPG paper, Guassian
| | | process in ours.
== ==================== ======== ================== ================================= =======================
"""
# You can refer to DDPG's default config for more details.
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='td3',
# (bool) Whether to use cuda for network.
cuda=False,
# (bool type) on_policy: Determine whether on-policy or off-policy.
# on-policy setting influences the behaviour of buffer.
# Default False in TD3.
on_policy=False,
# (bool) Whether use priority(priority sample, IS weight, update priority)
# Default False in TD3.
priority=False,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
# (int) Number of training samples(randomly collected) in replay buffer when training starts.
# Default 25000 in DDPG/TD3.
random_collect_size=25000,
model=dict(
# (bool) Whether to use two critic networks or only one.
# Clipped Double Q-Learning for Actor-Critic in original TD3 paper.
# Default True for TD3, False for DDPG.
twin_critic=True,
),
learn=dict(
multi_gpu=False,
# How many updates(iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
# collect data -> update policy-> collect data -> ...
update_per_collect=1,
# Minibatch size for gradient descent.
batch_size=256,
# Learning rates for actor network(aka. policy).
learning_rate_actor=1e-3,
# Learning rates and critic network(aka. Q-network).
learning_rate_critic=1e-3,
# (bool) Whether ignore done(usually for max step termination env. e.g. pendulum)
# Note: Gym wraps the MuJoCo envs by default with TimeLimit environment wrappers.
# These limit HalfCheetah, and several other MuJoCo envs, to max length of 1000.
# However, interaction with HalfCheetah always gets done with done is False,
# Since we inplace done==True with done==False to keep
# TD-error accurate computation(``gamma * (1 - done) * next_v + reward``),
# when the episode step is greater than max episode step.
ignore_done=False,
# (float type) target_theta: Used for soft update of the target network,
# aka. Interpolation factor in polyak averaging for target networks.
# Default to 0.005.
target_theta=0.005,
# (float) discount factor for the discounted sum of rewards, aka. gamma.
discount_factor=0.99,
# (int) When critic network updates once, how many times will actor network update.
# Delayed Policy Updates in original TD3 paper.
# Default 1 for DDPG, 2 for TD3.
actor_update_freq=2,
# (bool) Whether to add noise on target network's action.
# Target Policy Smoothing Regularization in original TD3 paper.
# Default True for TD3, False for DDPG.
noise=True,
# (float) Sigma for smoothing noise added to target policy.
noise_sigma=0.2,
# (dict) Limit for range of target policy smoothing noise, aka. noise_clip.
noise_range=dict(
min=-0.5,
max=0.5,
),
),
collect=dict(
# n_sample=1,
# (int) Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
# It is a must to add noise during collection. So here omits "noise" and only set "noise_sigma".
noise_sigma=0.1,
),
other=dict(
replay_buffer=dict(
# (int) Maximum size of replay buffer
replay_buffer_size=1000000,
),
),
)