Вы можете получить доступ к action словарю в объекте sample_batch, sample_obj.columns(["actions"] в on_postprocess_traj обратном вызове определить следующим образом:
# The callback function
def on_postprocess_traj(info):
"""
arg: {"agent_id": ..., "episode": ...,
"pre_batch": (before processing),
"post_batch": (after processing),
"all_pre_batches": (other agent ids),
}
# https://github.com/ray-project/ray/blob/ee8c9ff7320ec6a2d7d097cd5532005c6aeb216e/rllib/policy/sample_batch.py
Dictionaries in a sample_obj, k:
t
eps_id
agent_index
obs
actions
rewards
prev_actions
prev_rewards
dones
infos
new_obs
action_prob
action_logp
vf_preds
behaviour_logits
unroll_id
"""
agt_id = info["agent_id"]
eps_id = info["episode"].episode_id
policy_obj = info["pre_batch"][0]
sample_obj = info["pre_batch"][1]
if(agt_id == 'player1'):
print('agent_id = {}'.format(agt_id))
print('episode = {}'.format(eps_id))
#print("on_postprocess_traj info = {}".format(info))
#print("on_postprocess_traj sample_obj = {}".format(sample_obj))
print('actions = {}'.format(sample_obj.columns(["actions"])))
return
Вам также нужно будет добавить функцию обратного вызова в свою конфигурацию следующим образом:
config={"env": RockPaperScissorsEnv,
#"eager": True,
"gamma": 0.9,
"num_workers": 1,
"num_envs_per_worker": 4,
"sample_batch_size": 10,
"train_batch_size": 200,
#"multiagent": {"policies_to_train": ["learned"],
"multiagent": {"policies_to_train": ["learned", "learned_2"],
"policies": {"always_same": (AlwaysSameHeuristic, Discrete(3), Discrete(3), {}),
#"beat_last": (BeatLastHeuristic, Discrete(3), Discrete(3), {}),
"learned": (None, Discrete(3), Discrete(3), {"model": {"use_lstm": use_lstm}}),
"learned_2": (None, Discrete(3), Discrete(3), {"model": {"use_lstm": use_lstm}}),
},
"policy_mapping_fn": select_policy,
},
"callbacks": {#"on_episode_start": on_episode_start,
#"on_episode_step": on_episode_step,
#"on_episode_end": on_episode_end,
#"on_sample_end": on_sample_end,
"on_postprocess_traj": on_postprocess_traj,
#"on_train_result": on_train_result,
}
Результат ниже показывает результат выполнения примера rock_paper_scissors_multiagent.py (с ray[rllib]==0.8.2 в Colab), обратите внимание на распечатку идентификатора агента, идентификатора эпизода и траектории действия:
== Status ==
Memory usage on this node: 1.3/12.7 GiB
Using FIFO scheduling algorithm.
Resources requested: 2/2 CPUs, 0/0 GPUs, 0.0/7.18 GiB heap, 0.0/2.44 GiB objects
Result logdir: /root/ray_results/PPO
Number of trials: 1 (1 RUNNING)
Trial name status loc
PPO_RockPaperScissorsEnv_979bff44 RUNNING
(pid=1541) 2020-04-25 12:45:10,823 INFO trainer.py:420 -- Tip: set 'eager': true or the --eager flag to enable TensorFlow eager execution
(pid=1541) 2020-04-25 12:45:10,827 INFO trainer.py:580 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.
(pid=1541) /usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
(pid=1541) warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
(pid=1587) /usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
(pid=1587) warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
(pid=1541) 2020-04-25 12:45:19,048 WARNING util.py:37 -- Install gputil for GPU system monitoring.
(pid=1587) agent_id = player1
(pid=1587) episode = 975148816
(pid=1587) actions = [array([1, 1, 0, 2, 0, 0, 1, 2, 1, 2])]
(pid=1587) agent_id = player1
(pid=1587) episode = 942369634
(pid=1587) actions = [array([1, 2, 1, 2, 2, 2, 1, 0, 2, 0])]
(pid=1587) agent_id = player1
(pid=1587) episode = 296105405
(pid=1587) actions = [array([2, 2, 0, 2, 2, 1, 2, 1, 0, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 475466940
(pid=1587) actions = [array([0, 2, 1, 0, 2, 0, 2, 1, 0, 2])]
(pid=1587) agent_id = player1
(pid=1587) episode = 793839240
(pid=1587) actions = [array([0, 0, 1, 2, 0, 2, 1, 1, 1, 2])]
(pid=1587) agent_id = player1
(pid=1587) episode = 578652318
(pid=1587) actions = [array([0, 1, 0, 0, 2, 1, 2, 2, 1, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 112165627
(pid=1587) actions = [array([2, 1, 2, 1, 0, 0, 0, 1, 1, 0]
(pid=1587) agent_id = player1
(pid=1587) episode = 996828544
(pid=1587) actions = [array([1, 2, 2, 2, 0, 0, 1, 2, 0, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 94669775
(pid=1587) actions = [array([1, 0, 1, 1, 2, 0, 2, 1, 2, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1063457620
(pid=1587) actions = [array([1, 0, 2, 1, 2, 2, 1, 2, 2, 0])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1956229719
(pid=1587) actions = [array([0, 0, 2, 1, 2, 2, 2, 1, 2, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 503578202
(pid=1587) actions = [array([1, 2, 0, 0, 0, 0, 1, 0, 0, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1599756661
(pid=1587) actions = [array([0, 0, 1, 2, 0, 2, 2, 2, 1, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1333277267
(pid=1587) actions = [array([0, 2, 1, 0, 1, 1, 2, 2, 2, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1832916757
(pid=1587) actions = [array([1, 1, 0, 0, 2, 1, 0, 1, 1, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 585983090
(pid=1587) actions = [array([1, 2, 1, 2, 2, 1, 0, 2, 0, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 1731969708
(pid=1587) actions = [array([2, 1, 0, 2, 2, 0, 0, 0, 1, 0])]
(pid=1587) agent_id = player1
(pid=1587) episode = 374111939
(pid=1587) actions = [array([0, 0, 0, 2, 0, 2, 2, 0, 1, 0])]
(pid=1587) agent_id = player1
(pid=1587) episode = 399432786
(pid=1587) actions = [array([0, 2, 0, 0, 0, 1, 0, 0, 1, 1])]
(pid=1587) agent_id = player1
(pid=1587) episode = 396598872
(pid=1587) actions = [array([1, 1, 0, 2, 0, 2, 0, 2, 1, 0])]
Result for PPO_RockPaperScissorsEnv_979bff44:
custom_metrics: {}
date: 2020-04-25_12-45-24
done: true
episode_len_mean: 10.0
episode_reward_max: 0.0
episode_reward_mean: 0.0
episode_reward_min: 0.0
episodes_this_iter: 20
episodes_total: 20
experiment_id: 87214df9c01d4efeae8edd4d656a6ca4
experiment_tag: '0'
hostname: 2ebf5ae102f8
info:
grad_time_ms: 1005.051
learner:
learned:
cur_kl_coeff: 0.20000000298023224
cur_lr: 4.999999873689376e-05
entropy: 1.0945309400558472
entropy_coeff: 0.0
kl: 0.004110474139451981
policy_loss: -0.0945899486541748
total_loss: 2.941073417663574
vf_explained_var: 0.00013327598571777344
vf_loss: 3.034841299057007
learned_2:
cur_kl_coeff: 0.20000000298023224
cur_lr: 4.999999873689376e-05
entropy: 1.0941331386566162
entropy_coeff: 0.0
kl: 0.004472262226045132
policy_loss: -0.0190987978130579
total_loss: 3.0051088333129883
vf_explained_var: 0.008207857608795166
vf_loss: 3.023313045501709
load_time_ms: 179.466
num_steps_sampled: 200
num_steps_trained: 128
sample_time_ms: 343.341
update_time_ms: 2861.349
iterations_since_restore: 1
node_ip: 172.28.0.2
num_healthy_workers: 1
off_policy_estimator: {}
perf:
cpu_util_percent: 85.65
ram_util_percent: 16.225
pid: 1541
policy_reward_max:
learned: 6.0
learned_2: 6.0
policy_reward_mean:
learned: -0.15
learned_2: 0.15
policy_reward_min:
learned: -6.0
learned_2: -6.0
sampler_perf:
mean_env_wait_ms: 0.062040254181506584
mean_inference_ms: 3.5300535314223347
mean_processing_ms: 1.2217222475538068
time_since_restore: 4.562142610549927
time_this_iter_s: 4.562142610549927
time_total_s: 4.562142610549927
timestamp: 1587818724
timesteps_since_restore: 200
timesteps_this_iter: 200
timesteps_total: 200
training_iteration: 1
trial_id: 979bff44
== Status ==
Memory usage on this node: 2.0/12.7 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/2 CPUs, 0/0 GPUs, 0.0/7.18 GiB heap, 0.0/2.44 GiB objects
Result logdir: /root/ray_results/PPO
Number of trials: 1 (1 TERMINATED)
Trial name status loc reward total time (s) ts iter
PPO_RockPaperScissorsEnv_979bff44 TERMINATED 0 4.56214 200 1
== Status ==
Memory usage on this node: 1.9/12.7 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/2 CPUs, 0/0 GPUs, 0.0/7.18 GiB heap, 0.0/2.44 GiB objects
Result logdir: /root/ray_results/PPO
Number of trials: 1 (1 TERMINATED)
Trial name status loc reward total time (s) ts iter
PPO_RockPaperScissorsEnv_979bff44 TERMINATED 0 4.56214 200 1
2020-04-25 12:45:24,345 INFO tune.py:352 -- Returning an analysis object by default. You can call `analysis.trials` to retrieve a list of trials. This message will be removed in future versions of Tune.
Вы можете не только получить доступ к действиям, но и таким образом получить доступ ко всей полезной информации до / после пакетной обработки (траектории). Взгляните на комментарии, которые я сделал в функции обратного вызова, чтобы получить список доступных имен словарей (например, obs, rewards), которые также могут оказаться полезными.
Полный rock_paper_scissors_multiagent.py пример кода, который печатает вышеприведенный вывод, показан ниже:
#!pip install ray[rllib]==0.8.2
"""A simple multi-agent env with two agents playing rock paper scissors.
This demonstrates running the following policies in competition:
(1) heuristic policy of repeating the same move
(2) heuristic policy of beating the last opponent move
(3) LSTM/feedforward PG policies
(4) LSTM policy with custom entropy loss
"""
import argparse
import random
from gym.spaces import Discrete
from ray import tune
from ray.rllib.agents.pg.pg import PGTrainer
from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
from ray.rllib.policy.policy import Policy
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
ROCK = 0
PAPER = 1
SCISSORS = 2
parser = argparse.ArgumentParser()
parser.add_argument("--stop", type=int, default=400000)
class RockPaperScissorsEnv(MultiAgentEnv):
"""Two-player environment for rock paper scissors.
The observation is simply the last opponent action."""
def __init__(self, _):
self.action_space = Discrete(3)
self.observation_space = Discrete(3)
self.player1 = "player1"
self.player2 = "player2"
self.last_move = None
self.num_moves = 0
def reset(self):
self.last_move = (0, 0)
self.num_moves = 0
return {
self.player1: self.last_move[1],
self.player2: self.last_move[0],
}
def step(self, action_dict):
move1 = action_dict[self.player1]
move2 = action_dict[self.player2]
self.last_move = (move1, move2)
obs = {
self.player1: self.last_move[1],
self.player2: self.last_move[0],
}
r1, r2 = {
(ROCK, ROCK): (0, 0),
(ROCK, PAPER): (-1, 1),
(ROCK, SCISSORS): (1, -1),
(PAPER, ROCK): (1, -1),
(PAPER, PAPER): (0, 0),
(PAPER, SCISSORS): (-1, 1),
(SCISSORS, ROCK): (-1, 1),
(SCISSORS, PAPER): (1, -1),
(SCISSORS, SCISSORS): (0, 0),
}[move1, move2]
rew = {
self.player1: r1,
self.player2: r2,
}
self.num_moves += 1
done = {
"__all__": self.num_moves >= 10,
}
#print('obs', obs)
return obs, rew, done, {}
class AlwaysSameHeuristic(Policy):
"""Pick a random move and stick with it for the entire episode."""
def get_initial_state(self):
return [random.choice([ROCK, PAPER, SCISSORS])]
def compute_actions(self,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
**kwargs):
return list(state_batches[0]), state_batches, {}
def learn_on_batch(self, samples):
pass
def get_weights(self):
pass
def set_weights(self, weights):
pass
class BeatLastHeuristic(Policy):
"""Play the move that would beat the last move of the opponent."""
def compute_actions(self,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
**kwargs):
def successor(x):
if x[ROCK] == 1:
return PAPER
elif x[PAPER] == 1:
return SCISSORS
elif x[SCISSORS] == 1:
return ROCK
return [successor(x) for x in obs_batch], [], {}
def learn_on_batch(self, samples):
pass
def get_weights(self):
pass
def set_weights(self, weights):
pass
def on_postprocess_traj(info):
"""
arg: {"agent_id": ..., "episode": ...,
"pre_batch": (before processing),
"post_batch": (after processing),
"all_pre_batches": (other agent ids),
}
# https://github.com/ray-project/ray/blob/ee8c9ff7320ec6a2d7d097cd5532005c6aeb216e/rllib/policy/sample_batch.py
Dictionaries in a sample_obj, k:
t
eps_id
agent_index
obs
actions
rewards
prev_actions
prev_rewards
dones
infos
new_obs
action_prob
action_logp
vf_preds
behaviour_logits
unroll_id
"""
agt_id = info["agent_id"]
eps_id = info["episode"].episode_id
policy_obj = info["pre_batch"][0]
sample_obj = info["pre_batch"][1]
if(agt_id == 'player1'):
print('agent_id = {}'.format(agt_id))
print('episode = {}'.format(eps_id))
#print("on_postprocess_traj info = {}".format(info))
#print("on_postprocess_traj sample_obj = {}".format(sample_obj))
print('actions = {}'.format(sample_obj.columns(["actions"])))
return
def run_same_policy():
"""Use the same policy for both agents (trivial case)."""
#tune.run("PG", config={"env": RockPaperScissorsEnv})
tune.run("PPO", config={"env": RockPaperScissorsEnv})
#def run_heuristic_vs_learned(use_lstm=False, trainer="PG"):
def run_heuristic_vs_learned(use_lstm=False, trainer="PPO"):
"""Run heuristic policies vs a learned agent.
The learned agent should eventually reach a reward of ~5 with
use_lstm=False, and ~7 with use_lstm=True. The reason the LSTM policy
can perform better is since it can distinguish between the always_same vs
beat_last heuristics.
"""
def select_policy(agent_id):
if agent_id == "player1":
return "learned"
elif agent_id == "player2":
return "learned_2"
else:
return random.choice(["always_same", "beat_last"])
#args = parser.parse_args()
tune.run(trainer,
#stop={"timesteps_total": args.stop},
#stop={"timesteps_total": 400000},
stop={"timesteps_total": 3},
config={"env": RockPaperScissorsEnv,
#"eager": True,
"gamma": 0.9,
"num_workers": 1,
"num_envs_per_worker": 4,
"sample_batch_size": 10,
"train_batch_size": 200,
#"multiagent": {"policies_to_train": ["learned"],
"multiagent": {"policies_to_train": ["learned", "learned_2"],
"policies": {"always_same": (AlwaysSameHeuristic, Discrete(3), Discrete(3), {}),
#"beat_last": (BeatLastHeuristic, Discrete(3), Discrete(3), {}),
"learned": (None, Discrete(3), Discrete(3), {"model": {"use_lstm": use_lstm}}),
"learned_2": (None, Discrete(3), Discrete(3), {"model": {"use_lstm": use_lstm}}),
},
"policy_mapping_fn": select_policy,
},
"callbacks": {#"on_episode_start": on_episode_start,
#"on_episode_step": on_episode_step,
#"on_episode_end": on_episode_end,
#"on_sample_end": on_sample_end,
"on_postprocess_traj": on_postprocess_traj,
#"on_train_result": on_train_result,
}
}
)
def run_with_custom_entropy_loss():
"""Example of customizing the loss function of an existing policy.
This performs about the same as the default loss does."""
def entropy_policy_gradient_loss(policy, model, dist_class, train_batch):
logits, _ = model.from_batch(train_batch)
action_dist = dist_class(logits, model)
return (-0.1 * action_dist.entropy() - tf.reduce_mean(
action_dist.logp(train_batch["actions"]) *
train_batch["advantages"]))
EntropyPolicy = PGTFPolicy.with_updates(
loss_fn=entropy_policy_gradient_loss)
EntropyLossPG = PGTrainer.with_updates(
name="EntropyPG", get_policy_class=lambda _: EntropyPolicy)
run_heuristic_vs_learned(use_lstm=True, trainer=EntropyLossPG)
'''
if __name__ == "__main__":
# run_same_policy()
# run_heuristic_vs_learned(use_lstm=False)
run_heuristic_vs_learned(use_lstm=False)
# run_with_custom_entropy_loss()
'''
#run_same_policy()
run_heuristic_vs_learned(use_lstm=False)
#run_with_custom_entropy_loss()
person
Huan
schedule
25.04.2020
