from typing import Union, List, Dict, Optional, Tuple, Any
import math
import time
import gymnasium as gym
import os
import numpy as np
import numpy.typing as npt
import gym_csle_stopping_game.constants.constants as env_constants
from csle_common.dao.emulation_config.emulation_env_config import EmulationEnvConfig
from csle_common.dao.simulation_config.simulation_env_config import SimulationEnvConfig
from csle_common.dao.training.experiment_config import ExperimentConfig
from csle_common.dao.training.experiment_execution import ExperimentExecution
from csle_common.dao.training.experiment_result import ExperimentResult
from csle_common.dao.training.agent_type import AgentType
from csle_common.util.experiment_util import ExperimentUtil
from csle_common.logging.log import Logger
from csle_common.dao.training.vector_policy import VectorPolicy
from csle_common.metastore.metastore_facade import MetastoreFacade
from csle_common.dao.jobs.training_job_config import TrainingJobConfig
from csle_common.util.general_util import GeneralUtil
from csle_common.dao.simulation_config.base_env import BaseEnv
from csle_agents.agents.base.base_agent import BaseAgent
import csle_agents.constants.constants as agents_constants
[docs]class FictitiousPlayAgent(BaseAgent):
"""
Fictitious Play Agent for Normal-form Games (Brown 1951)
"""
def __init__(self, simulation_env_config: SimulationEnvConfig,
experiment_config: ExperimentConfig, env: Optional[BaseEnv] = None,
emulation_env_config: Union[None, EmulationEnvConfig] = None,
training_job: Optional[TrainingJobConfig] = None, save_to_metastore: bool = True):
"""
Initializes the Fictitious play agent
:param simulation_env_config: the simulation env config
:param emulation_env_config: the emulation env config
:param experiment_config: the experiment config
:param env: (optional) the gym environment to use for simulation
:param training_job: (optional) a training job configuration
:param save_to_metastore: boolean flag that can be set to avoid saving results and progress to the metastore
"""
super().__init__(simulation_env_config=simulation_env_config, emulation_env_config=emulation_env_config,
experiment_config=experiment_config)
assert experiment_config.agent_type == AgentType.FICTITIOUS_PLAY
self.env = env
self.training_job = training_job
self.save_to_metastore = save_to_metastore
[docs] def train(self) -> ExperimentExecution:
"""
Performs the policy training for the given random seeds using fictitious play
:return: the training metrics and the trained policies
"""
pid = os.getpid()
# Initialize metrics
exp_result = ExperimentResult()
exp_result.plot_metrics.append(agents_constants.COMMON.AVERAGE_RETURN)
exp_result.plot_metrics.append(agents_constants.COMMON.RUNNING_AVERAGE_RETURN)
exp_result.plot_metrics.append(env_constants.ENV_METRICS.INTRUSION_LENGTH)
exp_result.plot_metrics.append(agents_constants.COMMON.RUNNING_AVERAGE_INTRUSION_LENGTH)
exp_result.plot_metrics.append(env_constants.ENV_METRICS.INTRUSION_START)
exp_result.plot_metrics.append(agents_constants.COMMON.RUNNING_AVERAGE_INTRUSION_START)
exp_result.plot_metrics.append(env_constants.ENV_METRICS.TIME_HORIZON)
exp_result.plot_metrics.append(agents_constants.COMMON.RUNNING_AVERAGE_TIME_HORIZON)
exp_result.plot_metrics.append(env_constants.ENV_METRICS.AVERAGE_UPPER_BOUND_RETURN)
exp_result.plot_metrics.append(env_constants.ENV_METRICS.AVERAGE_DEFENDER_BASELINE_STOP_ON_FIRST_ALERT_RETURN)
descr = f"Training of policies with the fictitious play algorithm using " \
f"simulation:{self.simulation_env_config.name}"
for seed in self.experiment_config.random_seeds:
exp_result.all_metrics[seed] = {}
exp_result.all_metrics[seed][agents_constants.COMMON.AVERAGE_RETURN] = []
exp_result.all_metrics[seed][agents_constants.COMMON.RUNNING_AVERAGE_RETURN] = []
exp_result.all_metrics[seed][agents_constants.COMMON.RUNNING_AVERAGE_INTRUSION_START] = []
exp_result.all_metrics[seed][agents_constants.COMMON.RUNNING_AVERAGE_TIME_HORIZON] = []
exp_result.all_metrics[seed][agents_constants.COMMON.RUNNING_AVERAGE_INTRUSION_LENGTH] = []
exp_result.all_metrics[seed][env_constants.ENV_METRICS.INTRUSION_START] = []
exp_result.all_metrics[seed][env_constants.ENV_METRICS.INTRUSION_LENGTH] = []
exp_result.all_metrics[seed][env_constants.ENV_METRICS.TIME_HORIZON] = []
exp_result.all_metrics[seed][env_constants.ENV_METRICS.AVERAGE_UPPER_BOUND_RETURN] = []
exp_result.all_metrics[seed][
env_constants.ENV_METRICS.AVERAGE_DEFENDER_BASELINE_STOP_ON_FIRST_ALERT_RETURN] = []
# Initialize training job
if self.training_job is None:
self.training_job = TrainingJobConfig(
simulation_env_name=self.simulation_env_config.name, experiment_config=self.experiment_config,
progress_percentage=0, pid=pid, experiment_result=exp_result,
emulation_env_name=None, simulation_traces=[],
num_cached_traces=agents_constants.COMMON.NUM_CACHED_SIMULATION_TRACES,
log_file_path=Logger.__call__().get_log_file_path(), descr=descr,
physical_host_ip=GeneralUtil.get_host_ip())
if self.save_to_metastore:
training_job_id = MetastoreFacade.save_training_job(training_job=self.training_job)
self.training_job.id = training_job_id
else:
self.training_job.pid = pid
self.training_job.progress_percentage = 0
self.training_job.experiment_result = exp_result
if self.save_to_metastore:
MetastoreFacade.update_training_job(training_job=self.training_job, id=self.training_job.id)
# Initialize execution result
ts = time.time()
emulation_name = None
if self.emulation_env_config is not None:
emulation_name = self.emulation_env_config.name
simulation_name = self.simulation_env_config.name
self.exp_execution = ExperimentExecution(
result=exp_result, config=self.experiment_config, timestamp=ts, emulation_name=emulation_name,
simulation_name=simulation_name, descr=descr, log_file_path=self.training_job.log_file_path)
if self.save_to_metastore:
exp_execution_id = MetastoreFacade.save_experiment_execution(self.exp_execution)
self.exp_execution.id = exp_execution_id
config = self.simulation_env_config.simulation_env_input_config
if self.env is None:
self.env = gym.make(self.simulation_env_config.gym_env_name, config=config)
for seed in self.experiment_config.random_seeds:
ExperimentUtil.set_seed(seed)
exp_result = self.fictitious_play(exp_result=exp_result, seed=seed, training_job=self.training_job,
random_seeds=self.experiment_config.random_seeds)
# Save latest trace
if self.save_to_metastore:
if self.env is not None and len(self.env.get_traces()) > 0:
MetastoreFacade.save_simulation_trace(self.env.get_traces()[-1])
self.env.reset_traces()
# Calculate average and std metrics
exp_result.avg_metrics = {}
exp_result.std_metrics = {}
for metric in exp_result.all_metrics[self.experiment_config.random_seeds[0]].keys():
value_vectors = []
for seed in self.experiment_config.random_seeds:
value_vectors.append(exp_result.all_metrics[seed][metric])
avg_metrics = []
std_metrics = []
for i in range(len(value_vectors[0])):
if type(value_vectors[0][0]) is int or type(value_vectors[0][0]) is float \
or type(value_vectors[0][0]) is np.int64 or type(value_vectors[0][0]) is np.float64:
seed_values = []
for seed_idx in range(len(self.experiment_config.random_seeds)):
seed_values.append(value_vectors[seed_idx][i])
avg = ExperimentUtil.mean_confidence_interval(
data=seed_values,
confidence=self.experiment_config.hparams[agents_constants.COMMON.CONFIDENCE_INTERVAL].value)[0]
if not math.isnan(avg):
avg_metrics.append(avg)
ci = ExperimentUtil.mean_confidence_interval(
data=seed_values,
confidence=self.experiment_config.hparams[agents_constants.COMMON.CONFIDENCE_INTERVAL].value)[1]
if not math.isnan(ci):
std_metrics.append(ci)
else:
std_metrics.append(-1)
else:
avg_metrics.append(-1)
std_metrics.append(-1)
exp_result.avg_metrics[metric] = avg_metrics
exp_result.std_metrics[metric] = std_metrics
traces = self.env.get_traces()
if len(traces) > 0 and self.save_to_metastore:
MetastoreFacade.save_simulation_trace(traces[-1])
ts = time.time()
self.exp_execution.timestamp = ts
self.exp_execution.result = exp_result
if self.save_to_metastore:
MetastoreFacade.update_experiment_execution(experiment_execution=self.exp_execution,
id=self.exp_execution.id)
return self.exp_execution
[docs] def hparam_names(self) -> List[str]:
"""
:return: a list with the hyperparameter names
"""
return [agents_constants.FICTITIOUS_PLAY.N, agents_constants.FICTITIOUS_PLAY.PAYOFF_MATRIX,
agents_constants.COMMON.EVAL_BATCH_SIZE,
agents_constants.COMMON.CONFIDENCE_INTERVAL,
agents_constants.COMMON.RUNNING_AVERAGE]
[docs] def fictitious_play(self, exp_result: ExperimentResult, seed: int,
training_job: TrainingJobConfig, random_seeds: List[int]) -> ExperimentResult:
"""
Runs the fictitious play algorithm
:param exp_result: the experiment result object to store the result
:param seed: the seed
:param training_job: the training job config
:param random_seeds: list of seeds
:return: the updated experiment result and the trained policy
"""
# Hyperparameters
N = self.experiment_config.hparams[agents_constants.FICTITIOUS_PLAY.N].value
payoff_matrix = np.array(self.experiment_config.hparams[agents_constants.FICTITIOUS_PLAY.PAYOFF_MATRIX].value)
p1_prior = self.experiment_config.hparams[agents_constants.FICTITIOUS_PLAY.PLAYER_1_PRIOR].value
p2_prior = self.experiment_config.hparams[agents_constants.FICTITIOUS_PLAY.PLAYER_2_PRIOR].value
p1_empirical_strategies = []
p2_empirical_strategies = []
p1_best_responses = []
p2_best_responses = []
p1_payoffs = []
p2_payoffs = []
p1_counts_list = []
p2_counts_list = []
p1_counts_list.append(p1_prior)
p2_counts_list.append(p2_prior)
p1_empirical_strategy = p1_prior
p2_empirical_strategy = p2_prior
J = 0.0
for i in range(N):
p1_empirical_strategy = self.compute_empirical_strategy(p1_counts_list[-1])
p2_empirical_strategy = self.compute_empirical_strategy(p2_counts_list[-1])
p1_best_response, p1_payoff = self.best_response(p2_empirical_strategy, A=payoff_matrix, maximize=True)
p2_best_response, p2_payoff = self.best_response(p1_empirical_strategy, A=payoff_matrix, maximize=False)
p1_empirical_strategies.append(p1_empirical_strategy)
p2_empirical_strategies.append(p2_empirical_strategy)
p1_best_responses.append(p1_best_response)
p2_best_responses.append(p2_best_response)
p1_payoffs.append(p1_payoff)
p2_payoffs.append(p2_payoff)
latest_counts_p1 = p1_counts_list[-1].copy()
latest_counts_p2 = p2_counts_list[-1].copy()
latest_counts_p1[p1_best_response] += 1
latest_counts_p2[p2_best_response] += 1
p1_counts_list.append(latest_counts_p1)
p2_counts_list.append(latest_counts_p2)
# Log average return
J = p1_payoff
# policy.avg_R = J
exp_result.all_metrics[seed][agents_constants.COMMON.AVERAGE_RETURN].append(J)
running_avg_J = ExperimentUtil.running_average(
exp_result.all_metrics[seed][agents_constants.COMMON.AVERAGE_RETURN],
self.experiment_config.hparams[agents_constants.COMMON.RUNNING_AVERAGE].value)
exp_result.all_metrics[seed][agents_constants.COMMON.RUNNING_AVERAGE_RETURN].append(running_avg_J)
if i % self.experiment_config.log_every == 0 and i > 0:
# Update training job
total_iterations = len(random_seeds) * N
iterations_done = (random_seeds.index(seed)) * N + i
progress = round(iterations_done / total_iterations, 2)
training_job.progress_percentage = progress
training_job.experiment_result = exp_result
if self.env is not None and len(self.env.get_traces()) > 0:
training_job.simulation_traces.append(self.env.get_traces()[-1])
if len(training_job.simulation_traces) > training_job.num_cached_traces:
training_job.simulation_traces = training_job.simulation_traces[1:]
if self.save_to_metastore:
MetastoreFacade.update_training_job(training_job=training_job, id=training_job.id)
# Update execution
ts = time.time()
self.exp_execution.timestamp = ts
self.exp_execution.result = exp_result
if self.save_to_metastore:
MetastoreFacade.update_experiment_execution(experiment_execution=self.exp_execution,
id=self.exp_execution.id)
Logger.__call__().get_logger().info(
f"[FICTITIOUS-PLAY] i: {i}, J:{J}, "
f"J_avg_{self.experiment_config.hparams[agents_constants.COMMON.RUNNING_AVERAGE].value}:"
f"{running_avg_J}")
p1_policy = VectorPolicy(policy_vector=p1_empirical_strategy, simulation_name=self.simulation_env_config.name,
player_type=self.experiment_config.player_type,
actions=list(range(len(p1_empirical_strategy))),
avg_R=J, agent_type=AgentType.FICTITIOUS_PLAY)
p2_policy = VectorPolicy(policy_vector=p2_empirical_strategy, simulation_name=self.simulation_env_config.name,
player_type=self.experiment_config.player_type,
actions=list(range(len(p2_empirical_strategy))),
avg_R=J,
agent_type=AgentType.FICTITIOUS_PLAY)
exp_result.policies[seed] = p1_policy
exp_result.policies[seed + 1] = p2_policy
# Save policy
if self.save_to_metastore:
MetastoreFacade.save_vector_policy(vector_policy=p1_policy)
MetastoreFacade.save_vector_policy(vector_policy=p2_policy)
return exp_result
[docs] def compute_empirical_strategy(self, counts) -> List[float]:
"""
Computes the empirical strategy from a list of counts
:param counts: the list of counts
:return: the empirical strategy
"""
s = []
counts_sum = sum(counts)
for i in range(len(counts)):
s.append(counts[i] / counts_sum)
return s
[docs] def best_response(self, p: List[float], A: npt.NDArray[Any], maximize: bool = True) \
-> Tuple[int, float]:
"""
Computes a best response against p
:param p: the opponents strategy vector
:param A: the payoff matrix
:param maximize: whether it is a maximizer player or minimizer player
:return: the best response action and its payoff (value)
"""
if maximize:
payoffs = A.dot(np.array(p))
br = np.argmax(payoffs)
return int(br), payoffs[int(br)]
else:
payoffs = np.array(p).transpose().dot(A)
br = np.argmin(payoffs)
return int(br), payoffs[int(br)]
[docs] @staticmethod
def update_metrics(metrics: Dict[str, List[Union[float, int]]], info: Dict[str, Union[float, int]]) \
-> Dict[str, List[Union[float, int]]]:
"""
Update a dict with aggregated metrics using new information from the environment
:param metrics: the dict with the aggregated metrics
:param info: the new information
:return: the updated dict
"""
for k, v in info.items():
if k in metrics:
metrics[k].append(round(v, 3))
else:
metrics[k] = [v]
return metrics
[docs] @staticmethod
def compute_avg_metrics(metrics: Dict[str, List[Union[float, int]]]) -> Dict[str, Union[float, int]]:
"""
Computes the average metrics of a dict with aggregated metrics
:param metrics: the dict with the aggregated metrics
:return: the average metrics
"""
avg_metrics = {}
for k, v in metrics.items():
avg = round(sum(v) / len(v), 2)
avg_metrics[k] = avg
return avg_metrics
[docs] @staticmethod
def round_vec(vec) -> List[float]:
"""
Rounds a vector to 3 decimals
:param vec: the vector to round
:return: the rounded vector
"""
return list(map(lambda x: round(x, 3), vec))