interact_reward.py

# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Teleop the agent and visualize the learned reward."""

from absl import app
from absl import flags
from configs.constants import EMBODIMENTS
from configs.constants import XMAGICAL_EMBODIMENT_TO_ENV_NAME
from ml_collections import config_flags
import utils
from xmagical.utils import KeyboardEnvInteractor

FLAGS = flags.FLAGS

flags.DEFINE_enum("embodiment", "longstick", EMBODIMENTS,
                  "The agent embodiment.")
flags.DEFINE_boolean(
    "exit_on_done", True,
    "By default, env will terminate if done is True. Set to False to interact "
    "for as long as you want and press esc key to exit.")

config_flags.DEFINE_config_file(
    "config",
    "base_configs/rl.py",
    "File path to the training hyperparameter configuration.",
)


def main(_):
  env_name = XMAGICAL_EMBODIMENT_TO_ENV_NAME[FLAGS.embodiment]
  env = utils.make_env(env_name, seed=0)

  # Reward learning wrapper.
  if FLAGS.config.reward_wrapper.pretrained_path is not None:
    env = utils.wrap_learned_reward(env, FLAGS.config)

  viewer = KeyboardEnvInteractor(action_dim=env.action_space.shape[0])

  env.reset()
  obs = env.render("rgb_array")
  viewer.imshow(obs)

  i = [0]
  rews = []

  def step(action):
    obs, rew, done, info = env.step(action)
    rews.append(rew)
    if obs.ndim != 3:
      obs = env.render("rgb_array")
    if done:
      print(f"Done, score {info['eval_score']:.2f}/1.00")
      print("Episode metrics: ")
      for k, v in info["episode"].items():
        print(f"\t{k}: {v}")
      if FLAGS.exit_on_done:
        return
    i[0] += 1
    return obs

  viewer.run_loop(step)

  utils.plot_reward(rews)


if __name__ == "__main__":
  app.run(main)