def main(env, ctrl_type, ctrl_args, overrides, logdir):
set_global_seeds(0)
ctrl_args = DotMap(**{key: val for (key, val) in ctrl_args})
cfg = create_config(env, ctrl_type, ctrl_args, overrides, logdir)
cfg.pprint()
assert ctrl_type == 'MPC'
cfg.exp_cfg.exp_cfg.policy = MPC(cfg.ctrl_cfg)
exp = MBExperiment(cfg.exp_cfg)
os.makedirs(exp.logdir)
with open(os.path.join(exp.logdir, "config.txt"), "w") as f:
f.write(pprint.pformat(cfg.toDict()))
exp.run_experiment()
def main(args):
#set_global_seeds(0)
cfg = create_config(args)
cfg.pprint()
assert args.ctrl_type == 'MPC'
cfg.exp_cfg.exp_cfg.policy = MPC(cfg.ctrl_cfg)
exp = MBExperiment(cfg.exp_cfg)
if args.load_model_dir is not None:
exp.policy.model.load_state_dict(
torch.load(os.path.join(args.load_model_dir, 'weights')))
if not os.path.exists(exp.logdir):
os.makedirs(exp.logdir)
with open(os.path.join(exp.logdir, "config.txt"), "w") as f:
f.write(pprint.pformat(cfg.toDict()))
exp.run_experiment()
def main(env, ctrl_type, ctrl_args, overrides, model_dir, logdir):
ctrl_args = DotMap(**{key: val for (key, val) in ctrl_args})
overrides.append(["ctrl_cfg.prop_cfg.model_init_cfg.model_dir", model_dir])
overrides.append(["ctrl_cfg.prop_cfg.model_init_cfg.load_model", "True"])
overrides.append(["ctrl_cfg.prop_cfg.model_pretrained", "True"])
overrides.append(["exp_cfg.exp_cfg.ninit_rollouts", "0"])
overrides.append(["exp_cfg.exp_cfg.ntrain_iters", "1"])
overrides.append(["exp_cfg.log_cfg.nrecord", "1"])
cfg = create_config(env, ctrl_type, ctrl_args, overrides, logdir)
cfg.pprint()
if ctrl_type == "MPC":
cfg.exp_cfg.exp_cfg.policy = MPC(cfg.ctrl_cfg)
exp = MBExperiment(cfg.exp_cfg)
os.makedirs(exp.logdir)
with open(os.path.join(exp.logdir, "config.txt"), "w") as f:
f.write(pprint.pformat(cfg.toDict()))
exp.run_experiment()
def main(args):
set_global_seeds(0)
cfg = create_config(args)
cfg.pprint()
# Set env for PointmassEnv
if (isinstance(cfg.ctrl_cfg.env, PointmassEnv)):
# Change optimizer to discrete CEM
cfg.ctrl_cfg.opt_cfg.mode = 'DCEM'
#assert args.ctrl_type == 'MPC'
if args.ctrl_type == 'PuP':
print("Using Pets-using-Pets Policy.")
cfg.exp_cfg.exp_cfg.policy = ExploreEnsembleVarianceMPC(cfg.ctrl_cfg)
elif args.ctrl_type == 'RND':
assert False, "JL: Not implemented fully yet!"
print("Using RND Policy.")
cfg.exp_cfg.exp_cfg.policy = ExploreRNDMPC(cfg.ctrl_cfg)
else:
print("Using default MPC Policy.")
cfg.exp_cfg.exp_cfg.policy = MPC(cfg.ctrl_cfg)
exp = MBExperiment(cfg.exp_cfg)
if args.load_model_dir is not None:
exp.policy.model.load_state_dict(
torch.load(os.path.join(args.load_model_dir, 'weights')))
if not os.path.exists(exp.logdir):
os.makedirs(exp.logdir)
os.makedirs(os.path.join(exp.logdir, "TRAIN"))
os.makedirs(os.path.join(exp.logdir, "ADAPT"))
with open(os.path.join(exp.logdir, "config.txt"), "w") as f:
f.write(pprint.pformat(cfg.toDict()))
exp.run_experiment()