import gym
import argparse
import os
from common.agents.ddpg.ddpg import DDPG
from common.agents.ddpg_actor import DDPGActor
from common.utils.visualization import Visualizer
from common.utils.sim_agent_helper import generate_simulator_agent
from common.utils.logging import setup_experiment_logs, reshow_hyperparameters, StatsLogger
from experiments.domainrand.args import get_args, check_args
if __name__ == '__main__':
args = get_args()
paths = setup_experiment_logs(args)
check_args(args)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
stats_logger = StatsLogger(args)
visualizer = Visualizer(randomized_env_id=args.randomized_eval_env_id,
seed=args.seed)
reference_env = gym.make(args.reference_env_id)
if args.freeze_agent:
# only need the actor
agent_policy = DDPGActor(
state_dim=reference_env.observation_space.shape[0],
from common.utils.visualization import Visualizer
from common.utils.logging import setup_experiment_logs, reshow_hyperparameters
from experiments.domainrand.args import get_args, check_args
from common.utils.rollout_evaluation import evaluate_policy
from common.envs.randomized_vecenv import make_vec_envs
NEVAL_EPISODES = 10
N_PROCESSES = 5
N_SEEDS = 5
if __name__ == '__main__':
args = get_args()
paths = setup_experiment_logs(experiment_name='unfreeze-policy', args=args)
check_args(args, experiment_name='unfreeze-policy')
reference_env = gym.make(args.reference_env_id)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
environment_prototype = 'Pusher3DOFGeneralization{}{}-v0'
rewards_grid = np.zeros((3, 3, 5, NEVAL_EPISODES))
finaldists_grid = np.zeros((3, 3, 5, NEVAL_EPISODES))
for i in range(3):
for j in range(3):
randomized_env = make_vec_envs(environment_prototype.format(i, j), args.seed + i + j, N_PROCESSES)
actor_paths = glob.glob(os.path.join(os.getcwd(), paths['paper'], 'best-seed*_actor.pth'))
learning_curves_combinations = combinations(learning_curves_files, 5)
generalization_combinations = combinations(generalization_files, 5)
agent_name_start = paper_path.find('v0') + 3
agent_name_end = paper_path.find('-exp')
agent_name = paper_path[agent_name_start:agent_name_end]
return agent_name, list(learning_curves_files), generalization_files
if __name__ == '__main__':
args = get_args()
experiment_name = 'unfreeze-policy' if not args.use_bootstrapping_results else 'bootstrapping'
paths = setup_experiment_logs(experiment_name=experiment_name, args=args)
check_args(args, experiment_name=experiment_name)
agent_name, learning_curves_files, generalization_files = get_converged_modelpaths(paths)
nseeds = len(learning_curves_files)
nmetrics = len(np.load(learning_curves_files[0]).files)
# Learning curves
# Find Max Length and resize each array to that length
# for combination in combinations: for lc in combination
# for i, learning_curves_files in enumerate(learning_curves_combinations):
# print(i, learning_curves_files, '\n\n')
max_length = 0
for lc in learning_curves_files: