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pcn.py
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pcn.py
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"""Pareto Conditioned Network. Code adapted from https://github.com/mathieu-reymond/pareto-conditioned-networks ."""
import heapq
import os
from abc import ABC
from dataclasses import dataclass
from typing import List, Optional, Type, Union
import gymnasium as gym
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import wandb
from morl_baselines.common.evaluation import log_all_multi_policy_metrics
from morl_baselines.common.morl_algorithm import MOAgent, MOPolicy
from morl_baselines.common.pareto import get_non_dominated_inds
from morl_baselines.common.performance_indicators import hypervolume
def crowding_distance(points):
"""Compute the crowding distance of a set of points."""
# first normalize across dimensions
points = (points - points.min(axis=0)) / (points.ptp(axis=0) + 1e-8)
# sort points per dimension
dim_sorted = np.argsort(points, axis=0)
point_sorted = np.take_along_axis(points, dim_sorted, axis=0)
# compute distances between lower and higher point
distances = np.abs(point_sorted[:-2] - point_sorted[2:])
# pad extrema's with 1, for each dimension
distances = np.pad(distances, ((1,), (0,)), constant_values=1)
# sum distances of each dimension of the same point
crowding = np.zeros(points.shape)
crowding[dim_sorted, np.arange(points.shape[-1])] = distances
crowding = np.sum(crowding, axis=-1)
return crowding
@dataclass
class Transition:
"""Transition dataclass."""
observation: np.ndarray
action: Union[float, int]
reward: np.ndarray
next_observation: np.ndarray
terminal: bool
class BasePCNModel(nn.Module, ABC):
"""Base Model for the PCN."""
def __init__(self, state_dim: int, action_dim: int, reward_dim: int, scaling_factor: np.ndarray, hidden_dim: int):
"""Initialize the PCN model."""
super().__init__()
self.state_dim = state_dim
self.action_dim = action_dim
self.reward_dim = reward_dim
self.scaling_factor = nn.Parameter(th.tensor(scaling_factor).float(), requires_grad=False)
self.hidden_dim = hidden_dim
def forward(self, state, desired_return, desired_horizon):
"""Return log-probabilities of actions or return action directly in case of continuous action space."""
c = th.cat((desired_return, desired_horizon), dim=-1)
# commands are scaled by a fixed factor
c = c * self.scaling_factor
s = self.s_emb(state.float())
c = self.c_emb(c)
# element-wise multiplication of state-embedding and command
prediction = self.fc(s * c)
return prediction
class DiscreteActionsDefaultModel(BasePCNModel):
"""Model for the PCN with discrete actions."""
def __init__(self, state_dim: int, action_dim: int, reward_dim: int, scaling_factor: np.ndarray, hidden_dim: int):
"""Initialize the PCN model for discrete actions."""
super().__init__(state_dim, action_dim, reward_dim, scaling_factor, hidden_dim)
self.s_emb = nn.Sequential(nn.Linear(self.state_dim, self.hidden_dim), nn.Sigmoid())
self.c_emb = nn.Sequential(nn.Linear(self.reward_dim + 1, self.hidden_dim), nn.Sigmoid())
self.fc = nn.Sequential(
nn.Linear(self.hidden_dim, self.hidden_dim),
nn.ReLU(),
nn.Linear(self.hidden_dim, self.action_dim),
nn.LogSoftmax(dim=1),
)
class ContinuousActionsDefaultModel(BasePCNModel):
"""Model for the PCN with continuous actions."""
def __init__(self, state_dim: int, action_dim: int, reward_dim: int, scaling_factor: np.ndarray, hidden_dim: int):
"""Initialize the PCN model for continuous actions."""
super().__init__(state_dim, action_dim, reward_dim, scaling_factor, hidden_dim)
self.s_emb = nn.Sequential(nn.Linear(self.state_dim, self.hidden_dim), nn.Sigmoid())
self.c_emb = nn.Sequential(nn.Linear(self.reward_dim + 1, self.hidden_dim), nn.Sigmoid())
self.fc = nn.Sequential(
nn.Linear(self.hidden_dim, self.hidden_dim),
nn.ReLU(),
nn.Linear(self.hidden_dim, self.action_dim),
)
class PCN(MOAgent, MOPolicy):
"""Pareto Conditioned Networks (PCN).
Reymond, M., Bargiacchi, E., & Nowé, A. (2022, May). Pareto Conditioned Networks.
In Proceedings of the 21st International Conference on Autonomous Agents
and Multiagent Systems (pp. 1110-1118).
https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1110.pdf
## Credits
This code is a refactor of the code from the authors of the paper, available at:
https://github.com/mathieu-reymond/pareto-conditioned-networks
"""
def __init__(
self,
env: Optional[gym.Env],
scaling_factor: np.ndarray,
learning_rate: float = 1e-3,
gamma: float = 1.0,
batch_size: int = 256,
hidden_dim: int = 64,
noise: float = 0.1,
project_name: str = "MORL-Baselines",
experiment_name: str = "PCN",
wandb_entity: Optional[str] = None,
log: bool = True,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
model_class: Optional[Type[BasePCNModel]] = None,
) -> None:
"""Initialize PCN agent.
Args:
env (Optional[gym.Env]): Gym environment.
scaling_factor (np.ndarray): Scaling factor for the desired return and horizon used in the model.
learning_rate (float, optional): Learning rate. Defaults to 1e-2.
gamma (float, optional): Discount factor. Defaults to 1.0.
batch_size (int, optional): Batch size. Defaults to 32.
hidden_dim (int, optional): Hidden dimension. Defaults to 64.
noise (float, optional): Standard deviation of the noise to add to the action in the continuous action case. Defaults to 0.1.
project_name (str, optional): Name of the project for wandb. Defaults to "MORL-Baselines".
experiment_name (str, optional): Name of the experiment for wandb. Defaults to "PCN".
wandb_entity (Optional[str], optional): Entity for wandb. Defaults to None.
log (bool, optional): Whether to log to wandb. Defaults to True.
seed (Optional[int], optional): Seed for reproducibility. Defaults to None.
device (Union[th.device, str], optional): Device to use. Defaults to "auto".
model_class (Optional[Type[BasePCNModel]], optional): Model class to use. Defaults to None.
"""
MOAgent.__init__(self, env, device=device, seed=seed)
MOPolicy.__init__(self, device)
self.experience_replay = [] # List of (distance, time_step, transition)
self.batch_size = batch_size
self.gamma = gamma
self.learning_rate = learning_rate
self.hidden_dim = hidden_dim
self.scaling_factor = scaling_factor
self.desired_return = None
self.desired_horizon = None
self.continuous_action = True if type(self.env.action_space) is gym.spaces.Box else False
self.noise = noise
if model_class and not issubclass(model_class, BasePCNModel):
raise ValueError("model_class must be a subclass of BasePCNModel")
if model_class is None:
if self.continuous_action:
model_class = ContinuousActionsDefaultModel
else:
model_class = DiscreteActionsDefaultModel
self.model = model_class(
self.observation_dim, self.action_dim, self.reward_dim, self.scaling_factor, hidden_dim=self.hidden_dim
).to(self.device)
self.opt = th.optim.Adam(self.model.parameters(), lr=self.learning_rate)
self.log = log
if log:
experiment_name += " continuous action" if self.continuous_action else ""
self.setup_wandb(project_name, experiment_name, wandb_entity)
def get_config(self) -> dict:
"""Get configuration of PCN model."""
return {
"env_id": self.env.unwrapped.spec.id,
"batch_size": self.batch_size,
"gamma": self.gamma,
"learning_rate": self.learning_rate,
"hidden_dim": self.hidden_dim,
"scaling_factor": self.scaling_factor,
"continuous_action": self.continuous_action,
"noise": self.noise,
"seed": self.seed,
}
def update(self):
"""Update PCN model."""
batch = []
# randomly choose episodes from experience buffer
s_i = self.np_random.choice(np.arange(len(self.experience_replay)), size=self.batch_size, replace=True)
for i in s_i:
# episode is tuple (return, transitions)
ep = self.experience_replay[i][2]
# choose random timestep from episode,
# use it's return and leftover timesteps as desired return and horizon
t = self.np_random.integers(0, len(ep))
# reward contains return until end of episode
s_t, a_t, r_t, h_t = ep[t].observation, ep[t].action, np.float32(ep[t].reward), np.float32(len(ep) - t)
batch.append((s_t, a_t, r_t, h_t))
obs, actions, desired_return, desired_horizon = zip(*batch)
prediction = self.model(
th.tensor(obs).to(self.device),
th.tensor(desired_return).to(self.device),
th.tensor(desired_horizon).unsqueeze(1).to(self.device),
)
self.opt.zero_grad()
if self.continuous_action:
l = F.mse_loss(th.tensor(actions).float().to(self.device), prediction)
else:
# one-hot of action for CE loss
actions = F.one_hot(th.tensor(actions).long().to(self.device), len(prediction[0]))
# cross-entropy loss
l = th.sum(-actions * prediction, -1)
l = l.mean()
l.backward()
self.opt.step()
return l, prediction
def _add_episode(self, transitions: List[Transition], max_size: int, step: int) -> None:
# compute return
for i in reversed(range(len(transitions) - 1)):
transitions[i].reward += self.gamma * transitions[i + 1].reward
# pop smallest episode of heap if full, add new episode
# heap is sorted by negative distance, (updated in nlargest)
# put positive number to ensure that new item stays in the heap
if len(self.experience_replay) == max_size:
heapq.heappushpop(self.experience_replay, (1, step, transitions))
else:
heapq.heappush(self.experience_replay, (1, step, transitions))
def _nlargest(self, n, threshold=0.2):
"""See Section 4.4 of https://arxiv.org/pdf/2204.05036.pdf for details."""
returns = np.array([e[2][0].reward for e in self.experience_replay])
# crowding distance of each point, check ones that are too close together
distances = crowding_distance(returns)
sma = np.argwhere(distances <= threshold).flatten()
non_dominated_i = get_non_dominated_inds(returns)
non_dominated = returns[non_dominated_i]
# we will compute distance of each point with each non-dominated point,
# duplicate each point with number of non_dominated to compute respective distance
returns_exp = np.tile(np.expand_dims(returns, 1), (1, len(non_dominated), 1))
# distance to closest non_dominated point
l2 = np.min(np.linalg.norm(returns_exp - non_dominated, axis=-1), axis=-1) * -1
# all points that are too close together (crowding distance < threshold) get a penalty
non_dominated_i = np.nonzero(non_dominated_i)[0]
_, unique_i = np.unique(non_dominated, axis=0, return_index=True)
unique_i = non_dominated_i[unique_i]
duplicates = np.ones(len(l2), dtype=bool)
duplicates[unique_i] = False
l2[duplicates] -= 1e-5
l2[sma] *= 2
sorted_i = np.argsort(l2)
largest = [self.experience_replay[i] for i in sorted_i[-n:]]
# before returning largest elements, update all distances in heap
for i in range(len(l2)):
self.experience_replay[i] = (l2[i], self.experience_replay[i][1], self.experience_replay[i][2])
heapq.heapify(self.experience_replay)
return largest
def _choose_commands(self, num_episodes: int):
# get best episodes, according to their crowding distance
episodes = self._nlargest(num_episodes)
returns, horizons = list(zip(*[(e[2][0].reward, len(e[2])) for e in episodes]))
# keep only non-dominated returns
nd_i = get_non_dominated_inds(np.array(returns))
returns = np.array(returns)[nd_i]
horizons = np.array(horizons)[nd_i]
# pick random return from random best episode
r_i = self.np_random.integers(0, len(returns))
desired_horizon = np.float32(horizons[r_i] - 2)
# mean and std per objective
_, s = np.mean(returns, axis=0), np.std(returns, axis=0)
# desired return is sampled from [M, M+S], to try to do better than mean return
desired_return = returns[r_i].copy()
# random objective
r_i = self.np_random.integers(0, len(desired_return))
desired_return[r_i] += self.np_random.uniform(high=s[r_i])
desired_return = np.float32(desired_return)
return desired_return, desired_horizon
def _act(self, obs: np.ndarray, desired_return, desired_horizon, eval_mode=False) -> int:
prediction = self.model(
th.tensor([obs]).float().to(self.device),
th.tensor([desired_return]).float().to(self.device),
th.tensor([desired_horizon]).unsqueeze(1).float().to(self.device),
)
if self.continuous_action:
action = prediction.detach().cpu().numpy()[0]
if not eval_mode:
# Add Gaussian noise: https://arxiv.org/pdf/2204.05027.pdf
action = action + np.random.normal(0.0, self.noise)
return action
else:
log_probs = prediction.detach().cpu().numpy()[0]
if eval_mode:
action = np.argmax(log_probs)
else:
action = self.np_random.choice(np.arange(len(log_probs)), p=np.exp(log_probs))
return action
def _run_episode(self, env, desired_return, desired_horizon, max_return, eval_mode=False):
transitions = []
obs, _ = env.reset()
done = False
while not done:
action = self._act(obs, desired_return, desired_horizon, eval_mode)
n_obs, reward, terminated, truncated, _ = env.step(action)
done = terminated or truncated
transitions.append(
Transition(
observation=obs,
action=action,
reward=np.float32(reward).copy(),
next_observation=n_obs,
terminal=terminated,
)
)
obs = n_obs
# clip desired return, to return-upper-bound,
# to avoid negative returns giving impossible desired returns
desired_return = np.clip(desired_return - reward, None, max_return, dtype=np.float32)
# clip desired horizon to avoid negative horizons
desired_horizon = np.float32(max(desired_horizon - 1, 1.0))
return transitions
def set_desired_return_and_horizon(self, desired_return: np.ndarray, desired_horizon: int):
"""Set desired return and horizon for evaluation."""
self.desired_return = desired_return
self.desired_horizon = desired_horizon
def eval(self, obs, w=None):
"""Evaluate policy action for a given observation."""
return self._act(obs, self.desired_return, self.desired_horizon, eval_mode=True)
def evaluate(self, env, max_return, n=10):
"""Evaluate policy in the given environment."""
n = min(n, len(self.experience_replay))
episodes = self._nlargest(n)
returns, horizons = list(zip(*[(e[2][0].reward, len(e[2])) for e in episodes]))
returns = np.float32(returns)
horizons = np.float32(horizons)
e_returns = []
for i in range(n):
transitions = self._run_episode(env, returns[i], np.float32(horizons[i]), max_return, eval_mode=True)
# compute return
for i in reversed(range(len(transitions) - 1)):
transitions[i].reward += self.gamma * transitions[i + 1].reward
e_returns.append(transitions[0].reward)
distances = np.linalg.norm(np.array(returns) - np.array(e_returns), axis=-1)
return e_returns, np.array(returns), distances
def save(self, filename: str = "PCN_model", savedir: str = "weights"):
"""Save PCN."""
if not os.path.isdir(savedir):
os.makedirs(savedir)
th.save(self.model, f"{savedir}/{filename}.pt")
def train(
self,
total_timesteps: int,
eval_env: gym.Env,
ref_point: np.ndarray,
known_pareto_front: Optional[List[np.ndarray]] = None,
num_eval_weights_for_eval: int = 50,
num_er_episodes: int = 20,
num_step_episodes: int = 10,
num_model_updates: int = 50,
max_return: np.ndarray = None,
max_buffer_size: int = 100,
num_points_pf: int = 100,
):
"""Train PCN.
Args:
total_timesteps: total number of time steps to train for
eval_env: environment for evaluation
ref_point: reference point for hypervolume calculation
known_pareto_front: Optimal pareto front for metrics calculation, if known.
num_eval_weights_for_eval (int): Number of weights use when evaluating the Pareto front, e.g., for computing expected utility.
num_er_episodes: number of episodes to fill experience replay buffer
num_step_episodes: number of steps per episode
num_model_updates: number of model updates per episode
max_return: maximum return for clipping desired return. When None, this will be set to 100 for all objectives.
max_buffer_size: maximum buffer size
num_points_pf: number of points to sample from pareto front for metrics calculation
"""
max_return = max_return if max_return is not None else np.full(self.reward_dim, 100.0, dtype=np.float32)
if self.log:
self.register_additional_config(
{
"total_timesteps": total_timesteps,
"ref_point": ref_point.tolist(),
"known_front": known_pareto_front,
"num_eval_weights_for_eval": num_eval_weights_for_eval,
"num_er_episodes": num_er_episodes,
"num_step_episodes": num_step_episodes,
"num_model_updates": num_model_updates,
"max_return": max_return.tolist(),
"max_buffer_size": max_buffer_size,
"num_points_pf": num_points_pf,
}
)
self.global_step = 0
total_episodes = num_er_episodes
n_checkpoints = 0
# fill buffer with random episodes
self.experience_replay = []
for _ in range(num_er_episodes):
transitions = []
obs, _ = self.env.reset()
done = False
while not done:
action = self.env.action_space.sample()
n_obs, reward, terminated, truncated, _ = self.env.step(action)
transitions.append(Transition(obs, action, np.float32(reward).copy(), n_obs, terminated))
done = terminated or truncated
obs = n_obs
self.global_step += 1
# add episode in-place
self._add_episode(transitions, max_size=max_buffer_size, step=self.global_step)
while self.global_step < total_timesteps:
loss = []
entropy = []
for _ in range(num_model_updates):
l, lp = self.update()
loss.append(l.detach().cpu().numpy())
if not self.continuous_action:
lp = lp.detach().cpu().numpy()
ent = np.sum(-np.exp(lp) * lp)
entropy.append(ent)
desired_return, desired_horizon = self._choose_commands(num_er_episodes)
# get all leaves, contain biggest elements, experience_replay got heapified in choose_commands
leaves_r = np.array([e[2][0].reward for e in self.experience_replay[len(self.experience_replay) // 2 :]])
# leaves_h = np.array([len(e[2]) for e in self.experience_replay[len(self.experience_replay) // 2 :]])
if self.log:
hv = hypervolume(ref_point, leaves_r)
hv_est = hv
wandb.log(
{
"train/hypervolume": hv_est,
"train/loss": np.mean(loss),
"global_step": self.global_step,
},
)
if not self.continuous_action:
wandb.log(
{
"train/entropy": np.mean(entropy),
"global_step": self.global_step,
},
)
returns = []
horizons = []
for _ in range(num_step_episodes):
transitions = self._run_episode(self.env, desired_return, desired_horizon, max_return)
self.global_step += len(transitions)
self._add_episode(transitions, max_size=max_buffer_size, step=self.global_step)
returns.append(transitions[0].reward)
horizons.append(len(transitions))
total_episodes += num_step_episodes
if self.log:
wandb.log(
{
"train/episode": total_episodes,
"train/horizon_desired": desired_horizon,
"train/mean_horizon_distance": np.linalg.norm(np.mean(horizons) - desired_horizon),
"global_step": self.global_step,
},
)
for i in range(self.reward_dim):
wandb.log(
{
f"train/desired_return_{i}": desired_return[i],
f"train/mean_return_{i}": np.mean(np.array(returns)[:, i]),
f"train/mean_return_distance_{i}": np.linalg.norm(
np.mean(np.array(returns)[:, i]) - desired_return[i]
),
"global_step": self.global_step,
},
)
print(
f"step {self.global_step} \t return {np.mean(returns, axis=0)}, ({np.std(returns, axis=0)}) \t loss {np.mean(loss):.3E} \t horizons {np.mean(horizons)}"
)
if self.global_step >= (n_checkpoints + 1) * total_timesteps / 1000:
self.save()
n_checkpoints += 1
e_returns, _, _ = self.evaluate(eval_env, max_return, n=num_points_pf)
if self.log:
log_all_multi_policy_metrics(
current_front=e_returns,
hv_ref_point=ref_point,
reward_dim=self.reward_dim,
global_step=self.global_step,
n_sample_weights=num_eval_weights_for_eval,
ref_front=known_pareto_front,
)