sp
/
tempestpy
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

added jpg remove on gif create

refactoring
Thomas Knoll 11 months ago
parent
e92a3c1cc6
commit
175171c035
3 changed files with 78 additions and 50 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 8
      examples/shields/rl/callbacks.py
  2. 115
      examples/shields/rl/checkpoint.py
  3. 5
      examples/shields/rl/utils.py

8
examples/shields/rl/callbacks.py
View File

@ -12,9 +12,10 @@ from ray.rllib.evaluation.episode import Episode
from ray.rllib.evaluation.episode_v2 import EpisodeV2
from ray.rllib.algorithms.callbacks import DefaultCallbacks, make_multi_callbacks
from ray.tune import Callback
import matplotlib.pyplot as plt
class CustomCallback(DefaultCallbacks):
def on_episode_start(self, *, worker: RolloutWorker, base_env: BaseEnv, policies: Dict[PolicyID, Policy], episode, env_index, **kwargs) -> None:
@ -27,11 +28,10 @@ class CustomCallback(DefaultCallbacks):
episode.hist_data["goals_reached"] = []
episode.hist_data["ran_into_adversary"] = []
def on_episode_step(self, *, worker: RolloutWorker, base_env: BaseEnv, policies, episode, env_index, **kwargs) -> None:
episode.user_data["count"] = episode.user_data["count"] + 1
env = base_env.get_sub_environments()[0]
# print(env.printGrid())
def on_episode_end(self, *, worker: RolloutWorker, base_env: BaseEnv, policies, episode, env_index, **kwargs) -> None:
@ -40,7 +40,7 @@ class CustomCallback(DefaultCallbacks):
agent_tile = env.grid.get(env.agent_pos[0], env.agent_pos[1])
ran_into_adversary = False
if hasattr(env, "adversaries"):
if hasattr(env, "adversaries") and env.adversaries:
adversaries = env.adversaries.values()
for adversary in adversaries:
if adversary.cur_pos[0] == env.agent_pos[0] and adversary.cur_pos[1] == env.agent_pos[1]:

115
examples/shields/rl/checkpoint.py
View File

@ -12,13 +12,14 @@ from ray.rllib.models import ModelCatalog
from ray.rllib.algorithms.algorithm import Algorithm
from torch_action_mask_model import TorchActionMaskModel
from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
from helpers import parse_arguments, create_log_dir, ShieldingConfig
from shieldhandlers import MiniGridShieldHandler, create_shield_query
from callbacks import MyCallbacks
from rllibutils import OneHotShieldingWrapper, MiniGridShieldingWrapper
from utils import parse_arguments, create_log_dir, ShieldingConfig
from utils import MiniGridShieldHandler, create_shield_query
from callbacks import CustomCallback
from ray.tune.logger import TBXLogger
import imageio
import os
import matplotlib.pyplot as plt
@ -32,8 +33,8 @@ def shielding_env_creater(config):
shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
env = gym.make(name)
env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query)
env = gym.make(name, randomize_start=False)
env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query, mask_actions=False)
# env = minigrid.wrappers.ImgObsWrapper(env)
# env = ImgObsWrapper(env)
env = OneHotShieldingWrapper(env,
@ -41,6 +42,8 @@ def shielding_env_creater(config):
framestack=framestack
)
env.randomize_start = False
return env
@ -62,43 +65,67 @@ register_minigrid_shielding_env(args)
# Use the Algorithm's `from_checkpoint` utility to get a new algo instance
# that has the exact same state as the old one, from which the checkpoint was
# created in the first place:
path_to_checkpoint = '/home/tknoll/Documents/Projects/log_results/PPO-shielding:full-evaluations:10-steps:20000-env:MiniGrid-LavaSlipperyS12-v2/PPO/PPO_mini-grid-shielding_8cd74_00000_0_2023-09-13_14-10-38/checkpoint_000005'
algo = Algorithm.from_checkpoint(path_to_checkpoint)
# Continue training.
name = "MiniGrid-LavaSlipperyS12-v2"
shield_creator = MiniGridShieldHandler(F"./{args.grid_path}_1.txt", args.grid_to_prism_binary_path, F"./{args.prism_path}_1.prism", args.formula)
env = gym.make(name)
env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query)
# env = minigrid.wrappers.ImgObsWrapper(env)
# env = ImgObsWrapper(env)
env = OneHotShieldingWrapper(env,
0,
framestack=4
)
episode_reward = 0
terminated = truncated = False
obs, info = env.reset()
i = 0
filenames = []
while not terminated and not truncated:
action = algo.compute_single_action(obs)
obs, reward, terminated, truncated, info = env.step(action)
episode_reward += reward
filename = F"./frames/{i}.jpg"
img = env.get_frame()
plt.imsave(filename, img)
filenames.append(filename)
i = i + 1
# checkpoints = [('/home/knolli/Documents/University/Thesis/log_results/sh:none-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_low.yaml/checkpoint_000030', 'No_shield'),
# ("/home/knolli/Documents/University/Thesis/log_results/Relative_06/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_high.yaml/checkpoint_000030", "Rel_06_high"),
# ("/home/knolli/Documents/University/Thesis/log_results/Relative_06/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_medium.yaml/checkpoint_000030", "Rel_06_med"),
# ("/home/knolli/Documents/University/Thesis/log_results/Relative_06/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_low.yaml/checkpoint_000030", "Rel_06_low"),
# ("/home/knolli/Documents/University/Thesis/log_results/RELATIVE_1/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_high.yaml/checkpoint_000016", "Rel_1_high"),
# ("/home/knolli/Documents/University/Thesis/log_results/RELATIVE_1/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_medium.yaml/checkpoint_000030", "Rel_1_med"),
# ("/home/knolli/Documents/University/Thesis/log_results/RELATIVE_1/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:adv_config_slippery_low.yaml/checkpoint_000030", "Rel_1_low")]
checkpoints = [
# ('/home/knolli/Documents/University/Thesis/log_results/sh:none-value:0.9-env:MiniGrid-LavaSlipperyS12-v2-conf:slippery_high_pro.yaml/checkpoint_000070', "no_shielding"),
# ('/home/knolli/Documents/University/Thesis/log_results/sh:full-value:0.9-env:MiniGrid-LavaSlipperyS12-v2-conf:slippery_high_pro.yaml/checkpoint_000070', "shielding_09"),
# ('/home/knolli/Documents/University/Thesis/log_results/sh:full-value:1.0-env:MiniGrid-LavaSlipperyS12-v2-conf:slippery_high_pro.yaml/checkpoint_000070', "shielding_1")]
('/home/knolli/Documents/University/Thesis/logresults/exp/trial_0_2024-01-09_22-39-43/checkpoint_000002', 'v3')]
# checkpoints = [('/home/knolli/Documents/University/Thesis/log_results/sh:full-env:MiniGrid-LavaSlipperyS12-v2-conf:slippery_high_prob.yaml/checkpoint_000060', "Shielded_Gif")]
for path_to_checkpoint, gif_name in checkpoints:
algo = Algorithm.from_checkpoint(path_to_checkpoint)
policy = algo.get_policy()
# Continue training.
name = "MiniGrid-LavaSlipperyS12-v0"
shield_creator = MiniGridShieldHandler(F"./{args.grid_path}_1.txt", args.grid_to_prism_binary_path, F"./{args.prism_path}_1.prism", args.formula)
env = gym.make(name, randomize_start=False, probability_forward=3/9, probability_direct_neighbour=5/9, probability_next_neighbour=7/9,)
env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query, mask_actions=True)
# env = minigrid.wrappers.ImgObsWrapper(env)
# env = ImgObsWrapper(env)
env = OneHotShieldingWrapper(env,
0,
framestack=4
)
episode_reward = 0
terminated = truncated = False
import imageio
images = []
for filename in filenames:
images.append(imageio.imread(filename))
imageio.mimsave('./movie.gif', images)
obs, info = env.reset()
i = 0
filenames = []
while not terminated and not truncated:
action = algo.compute_single_action(obs)
policy_actions = policy.compute_single_action(obs)
# print(f'Policy actions {policy_actions}')
# print(f'Policy actions {policy_actions.logits}')
policy_action = policy_actions[2]['action_dist_inputs'].argmax()
# print(f'The action is: {action} vs policy action {policy_action}')
if policy_action != action:
print('policy action deviated')
action = policy_action
obs, reward, terminated, truncated, info = env.step(action)
episode_reward += reward
filename = F"./frames/{i}.jpg"
img = env.get_frame()
plt.imsave(filename, img)
filenames.append(filename)
i = i + 1
import imageio
images = []
for filename in filenames:
images.append(imageio.imread(filename))
imageio.mimsave(F'./{gif_name}.gif', images)
for filename in filenames:
os.remove(filename)

5
examples/shields/rl/utils.py
View File

@ -235,7 +235,7 @@ def extract_doors(env):
def extract_adversaries(env):
adv = []
if not hasattr(env, "adversaries"):
if not hasattr(env, "adversaries") or not env.adversaries:
return []
for color, adversary in env.adversaries.items():
@ -286,7 +286,8 @@ def parse_arguments(argparse):
"MiniGrid-AdvSimple-8x8-v0",
"MiniGrid-LavaCrossingS9N1-v0",
"MiniGrid-LavaCrossingS9N3-v0",
"MiniGrid-LavaSlipperyCliffS12-v0"
"MiniGrid-LavaSlipperyCliffS12-v0",
"MiniGrid-LavaFaultyS12-30-v0",
])
# parser.add_argument("--seed", type=int, help="seed for environment", default=None)

Write Preview
Loading…
Cancel
Save