How to use load_environments method in lisa

Best Python code snippet using lisa_python

a_star_transfer_main.py

Source:a_star_transfer_main.py Github

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...157 models = {'final': ecco_pretrain.model, 'transfer': ecco_transfer.model,158 'explore': a_star.model}159 from env.env_utils import load_environments160 if args.load_environments is not None:161 environments_cache = load_environments(162 args.load_environments, args.num_cache, args.task,163 args.episode_length, args.seed164 )165 else:166 environments_cache = None167 train(dqn_transfer_trainer.trainer, dqn_transfer_task_sampler,168 dqn_transfer_worker, models, args, None,169 {'transfer_fnc': transfer, 'transfer_thread': a_star_transfer_trainer},170 environments_cache)171if __name__ == '__main__':...

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dqn_transfer_st_main.py

Source:dqn_transfer_st_main.py Github

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...154 models = {'final': ecco_pretrain.model, 'transfer': ecco_transfer.model,155 'base': base_model.model}156 from env.env_utils import load_environments157 if args.load_environments is not None:158 environments_cache = load_environments(159 args.load_environments, args.num_cache, args.task,160 args.episode_length, args.seed161 )162 else:163 environments_cache = None164 train(dqn_transfer_trainer.trainer, dqn_transfer_task_sampler, 165 dqn_transfer_worker, models, args,166 {'pretrain_fnc':pretrain, 'pretrain_thread': dqn_transfer_jwt},167 environments_cache)168 169if __name__ == '__main__':...

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vis.py

Source:vis.py Github

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...8from utils.load_envs import load_environments9from learning_algs.amp_models import ActorCriticNet10if __name__ == '__main__':11 args = load_args()12 # _, env, _ = load_environments(args)13 env = HumanoidTreadmillEnv(args=args)14 model = ActorCriticNet(env.observation_space.shape[0], env.action_space.shape[0], [128, 128])15 model.load_state_dict(torch.load('results/models/fc_test/fc_test_iter3400.pt')) # relative file path16 model.cuda()17 # print(env.action_space.shape[0])18 while True:19 state = env.reset()20 done = False21 while not done:22 with torch.no_grad():23 act = model.sample_best_actions(torch.from_numpy(state).cuda().type(torch.cuda.FloatTensor)).cpu().numpy()24 # act = np.zeros(env.action_space.shape[0])25 next_state, reward, done, info = env.step(act)26 # print("in loop")...

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