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dueling=True)
alg_kwargs.update(extra_args)
if args.network:
alg_kwargs['network'] = args.network
else:
if alg_kwargs.get('network') is None:
alg_kwargs['network'] = get_default_network(env_type)
print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))
with open(args.config, 'r') as finput:
config = json.load(finput)
config['max_timesteps_to_shape'] += alg_kwargs['total_timesteps']
print('Algorithm config is {}'.format(config))
seed = args.seed
learn(seed=seed, config=config, **alg_kwargs)