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("===========================")
def main():
env = 'MiniGrid-Empty-v0'
env = gym.make(env, size=ENV_SIZE)
env = ch.envs.Torch(env)
env = ch.envs.Runner(env)
env = Monitor(env,
"./vid",
video_callable=lambda episode_id: True,
force=True)
for task_config in env.sample_tasks(4):
env.reset_task(task_config)
env.reset()
transition = env.run(get_random_action, episodes=5, render=RENDER)
