Commit 20df7d27 authored by nilabha's avatar nilabha
Browse files

Update checkpoints, rollout scripts, add test_result script

parent 6e5f6dad
Pipeline #5251 failed with stage
in 2 minutes and 59 seconds
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import numpy as np
import pandas as pd
import os
import json
# The file all_eval_runs are generated by the
df_eval = pd.read_csv('all_eval_runs.csv')
df_test_results = df_eval[["run","group"]].drop_duplicates()
all_runs = df_test_results["run"].to_list()
colnames = ["run","percentage_complete_mean","normalized_reward_mean"]
df_test_metrics = pd.DataFrame(columns= colnames)
for cur_run in all_runs:
result_file = "checkpoints/"+ cur_run + "/test_outcome.json"
if os.path.isfile(result_file):
with open(result_file) as f:
data = json.load(f)
df_test_metrics = df_test_metrics.append({colnames[0]:cur_run,colnames[1]:data.get(colnames[1]),colnames[2]:data.get(colnames[2])},ignore_index = True)
df_test = pd.merge(df_test_metrics,df_test_results,how='left')
df_all_final_results = df_test.groupby("group").aggregate([np.mean,np.std]).reset_index()
......@@ -13,6 +13,7 @@ import gym
import numpy as np
import ray
from ray.rllib.agents.registry import get_agent_class
from ray.tune.registry import get_trainable_cls
from ray.rllib.env import MultiAgentEnv
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
# from ray.rllib.evaluation.episode import _flatten_action # ray 0.8.4
......@@ -20,7 +21,6 @@ from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.space_utils import flatten_to_single_ndarray # ray 0.8.5
from ray.tune.utils import merge_dicts
from algorithms.imitation_agent.imitation_trainer import ImitationAgent
from utils.loader import load_envs, load_models, load_algorithms
logger = logging.getLogger(__name__)
......@@ -44,6 +44,23 @@ load_models(os.getcwd()) # Load models
from algorithms import CUSTOM_ALGORITHMS
load_algorithms(CUSTOM_ALGORITHMS) # Load algorithms
from import Mapping
from copy import deepcopy
def val_replace(mapping):
obj = deepcopy(mapping)
if isinstance(mapping, Mapping):
for key, val in mapping.items():
obj[key] = val_replace(val)
if mapping == "False":
return False
if mapping == "True":
return True
return mapping
return obj
class RolloutSaver:
"""Utility class for storing rollouts.
......@@ -233,6 +250,10 @@ def create_parser(parser_creator=None):
help="Write progress to a temporary file (updated "
"after each episode). An output filename must be set using --out; "
"the progress file will live in the same folder.")
help="Whether to attempt to enable TF eager execution.")
return parser
......@@ -253,26 +274,22 @@ def run(args, parser):
config = pickle.load(f)
if "num_workers" in config:
config["num_workers"] = min(2, config["num_workers"])
config = merge_dicts(config, args.config)
updated_config = val_replace(args.config)
config = merge_dicts(config, updated_config)
if not args.env:
if not config.get("env"):
parser.error("the following arguments are required: --env")
args.env = config.get("env")
cls = get_agent_class(
cls = ImitationAgent # CUSTOM_ALGORITHMS[]
if args.eager:
from tensorflow.python.framework.ops import enable_eager_execution
config['model']['vf_share_layers'] = False
cls = get_trainable_cls(
agent = cls(env=args.env, config=config)
num_steps = int(args.steps)
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