def main():
env = gym.make(ENV_NAME)
env = Monitor(env, f"./videos/{ENV_PREFIX}", force=True)
policy, baseline = load_meta_learner_params(META_POLICY_PATH,
BASELINE_PATH, env)
sampler = BatchSampler(env_name=ENV_NAME, batch_size=20, num_workers=2)
learner = MetaLearner(sampler, policy, baseline, optimizer=None)
for task in TEST_TASKS:
returns = []
for i in range(1, EVAL_STEPS + 1):
for grad_steps in GRAD_STEPS:
if i % 10 == 0:
print(f"Evaluation-step: {i}")
env.reset_task(task)
learner.policy, learner.baseline = load_meta_learner_params(
META_POLICY_PATH, BASELINE_PATH, env)
# Sample a batch of transitions
sampler.reset_task(task)
episodes = sampler.sample(learner.policy)
for _ in range(grad_steps):
new_params = learner.adapt(episodes)
learner.policy.set_params_with_name(new_params)
returns.append(evaluate(env, task, learner.policy))
print("========EVAL RESULTS=======")
print(f"Task: {task}")
print(f"Returns: {returns}")
print(f"Average Return: {np.mean(returns)}")
print("===========================")
fast_lr=args.fast_lr,
tau=args.tau,
device=args.device)
env = gym.make(args.env_name)
# new task!
episodes = []
#randomly sample task
test_task = sampler.sample_tasks(num_tasks=1)
#set specific task.
#test_task = []
#test_task.append({'velocity': 1.9})
sampler.reset_task(test_task[0])
print("new task: ", test_task[0], ", where 1 is forward")
#task = env.unwrapped.sample_tasks(1)
env.unwrapped.reset_task(test_task[0])
observations = env.reset()
print("new task: ", env.step([1])[3]['task'], ", where 1 is forward")
_theta = env.step([1])[3]['task']
degrees = 180 * _theta['theta'] / np.pi
print("new task in degrees: ", degrees)
train_episodes = metalearner.sampler.sample(the_model,
gamma=args.gamma,
device=args.device)
print("len of train episoid: ", len(train_episodes))