from typing import Any, cast
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.algorithm import QRDQN
from tianshou.algorithm.modelfree.qrdqn import QRDQNPolicy
from tianshou.algorithm.modelfree.reinforce import SimpleLossTrainingStats
from tianshou.algorithm.optim import OptimizerFactory
from tianshou.data import Batch, to_numpy
from tianshou.data.batch import BatchProtocol
from tianshou.data.types import (
ObsBatchProtocol,
QuantileRegressionBatchProtocol,
RolloutBatchProtocol,
)
[docs]
class IQNPolicy(QRDQNPolicy):
def __init__(
self,
*,
model: torch.nn.Module,
action_space: gym.spaces.Space,
sample_size: int = 32,
online_sample_size: int = 8,
target_sample_size: int = 8,
observation_space: gym.Space | None = None,
eps_training: float = 0.0,
eps_inference: float = 0.0,
) -> None:
"""
:param model:
:param action_space: the environment's action space
:param sample_size:
:param online_sample_size:
:param target_sample_size:
:param observation_space: the environment's observation space
:param eps_training: the epsilon value for epsilon-greedy exploration during training.
When collecting data for training, this is the probability of choosing a random action
instead of the action chosen by the policy.
A value of 0.0 means no exploration (fully greedy) and a value of 1.0 means full
exploration (fully random).
:param eps_inference: the epsilon value for epsilon-greedy exploration during inference,
i.e. non-training cases (such as evaluation during test steps).
The epsilon value is the probability of choosing a random action instead of the action
chosen by the policy.
A value of 0.0 means no exploration (fully greedy) and a value of 1.0 means full
exploration (fully random).
"""
assert isinstance(action_space, gym.spaces.Discrete)
assert sample_size > 1, f"sample_size should be greater than 1 but got: {sample_size}"
assert online_sample_size > 1, (
f"online_sample_size should be greater than 1 but got: {online_sample_size}"
)
assert target_sample_size > 1, (
f"target_sample_size should be greater than 1 but got: {target_sample_size}"
)
super().__init__(
model=model,
action_space=action_space,
observation_space=observation_space,
eps_training=eps_training,
eps_inference=eps_inference,
)
self.sample_size = sample_size
self.online_sample_size = online_sample_size
self.target_sample_size = target_sample_size
[docs]
def forward(
self,
batch: ObsBatchProtocol,
state: dict | BatchProtocol | np.ndarray | None = None,
model: torch.nn.Module | None = None,
**kwargs: Any,
) -> QuantileRegressionBatchProtocol:
is_model_old = model is not None
if is_model_old:
sample_size = self.target_sample_size
elif self.training:
sample_size = self.online_sample_size
else:
sample_size = self.sample_size
if model is None:
model = self.model
obs = batch.obs
# TODO: this seems very contrived!
obs_next = obs.obs if hasattr(obs, "obs") else obs
(logits, taus), hidden = model(
obs_next,
sample_size=sample_size,
state=state,
info=batch.info,
)
q = self.compute_q_value(logits, getattr(obs, "mask", None))
act = to_numpy(q.max(dim=1)[1])
result = Batch(logits=logits, act=act, state=hidden, taus=taus)
return cast(QuantileRegressionBatchProtocol, result)
[docs]
class IQN(QRDQN[IQNPolicy]):
"""Implementation of Implicit Quantile Network. arXiv:1806.06923."""
def __init__(
self,
*,
policy: IQNPolicy,
optim: OptimizerFactory,
gamma: float = 0.99,
num_quantiles: int = 200,
n_step_return_horizon: int = 1,
target_update_freq: int = 0,
) -> None:
"""
:param policy: the policy
:param optim: the optimizer factory for the policy's model
:param gamma: the discount factor in [0, 1] for future rewards.
This determines how much future rewards are valued compared to immediate ones.
Lower values (closer to 0) make the agent focus on immediate rewards, creating "myopic"
behavior. Higher values (closer to 1) make the agent value long-term rewards more,
potentially improving performance in tasks where delayed rewards are important but
increasing training variance by incorporating more environmental stochasticity.
Typically set between 0.9 and 0.99 for most reinforcement learning tasks
:param num_quantiles: the number of quantile midpoints in the inverse
cumulative distribution function of the value.
:param n_step_return_horizon: the number of future steps (> 0) to consider when computing temporal
difference (TD) targets. Controls the balance between TD learning and Monte Carlo methods:
higher values reduce bias (by relying less on potentially inaccurate value estimates)
but increase variance (by incorporating more environmental stochasticity and reducing
the averaging effect). A value of 1 corresponds to standard TD learning with immediate
bootstrapping, while very large values approach Monte Carlo-like estimation that uses
complete episode returns.
:param target_update_freq: the number of training iterations between each complete update of
the target network.
Controls how frequently the target Q-network parameters are updated with the current
Q-network values.
A value of 0 disables the target network entirely, using only a single network for both
action selection and bootstrap targets.
Higher values provide more stable learning targets but slow down the propagation of new
value estimates. Lower positive values allow faster learning but may lead to instability
due to rapidly changing targets.
Typically set between 100-10000 for DQN variants, with exact values depending on environment
complexity.
"""
super().__init__(
policy=policy,
optim=optim,
gamma=gamma,
num_quantiles=num_quantiles,
n_step_return_horizon=n_step_return_horizon,
target_update_freq=target_update_freq,
)
def _update_with_batch(
self,
batch: RolloutBatchProtocol,
) -> SimpleLossTrainingStats:
self._periodically_update_lagged_network_weights()
weight = batch.pop("weight", 1.0)
action_batch = self.policy(batch)
curr_dist, taus = action_batch.logits, action_batch.taus
act = batch.act
curr_dist = curr_dist[np.arange(len(act)), act, :].unsqueeze(2)
target_dist = batch.returns.unsqueeze(1)
# calculate each element's difference between curr_dist and target_dist
dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
huber_loss = (
(
dist_diff
* (taus.unsqueeze(2) - (target_dist - curr_dist).detach().le(0.0).float()).abs()
)
.sum(-1)
.mean(1)
)
loss = (huber_loss * weight).mean()
# ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
# blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
batch.weight = dist_diff.detach().abs().sum(-1).mean(1) # prio-buffer
self.optim.step(loss)
return SimpleLossTrainingStats(loss=loss.item())
