DI-engine/dizoo/mujoco/envs/test/test_mujoco_env.py

import os
import pytest
import numpy as np
from easydict import EasyDict

from ding.utils import set_pkg_seed
from dizoo.mujoco.envs import MujocoEnv


@pytest.mark.envtest
@pytest.mark.parametrize('delay_reward_step', [1, 10])
def test_mujoco_env_delay_reward(delay_reward_step):
    set_pkg_seed(1234, use_cuda=False)
    env = MujocoEnv(
        EasyDict(
            {
                'env_id': 'Ant-v3',
                'action_clip': False,
                'delay_reward_step': delay_reward_step,
                'save_replay_gif': False,
                'replay_path_gif': None
            }
        )
    )
    env.seed(1234)
    env.reset()
    action_dim = env.action_space.shape
    for i in range(25):
        # Both ``env.random_action()``, and utilizing ``np.random`` as well as action space,
        # can generate legal random action.
        if i < 10:
            action = np.random.random(size=action_dim)
        else:
            action = env.random_action()
        timestep = env.step(action)
        print(timestep.reward)
        assert timestep.reward.shape == (1, ), timestep.reward.shape


@pytest.mark.envtest
def test_mujoco_env_eval_episode_return():
    set_pkg_seed(1234, use_cuda=False)
    env = MujocoEnv(
        EasyDict(
            {
                'env_id': 'Ant-v3',
                'action_clip': False,
                'delay_reward_step': 4,
                'save_replay_gif': False,
                'replay_path_gif': None
            }
        )
    )
    env.seed(1234)
    env.reset()
    action_dim = env.action_space.shape
    eval_episode_return = np.array([0.], dtype=np.float32)
    while True:
        action = np.random.random(size=action_dim)
        timestep = env.step(action)
        eval_episode_return += timestep.reward
        # print("{}(dtype: {})".format(timestep.reward, timestep.reward.dtype))
        if timestep.done:
            print(
                "{}({}), {}({})".format(
                    timestep.info['eval_episode_return'], type(timestep.info['eval_episode_return']),
                    eval_episode_return, type(eval_episode_return)
                )
            )
            # timestep.reward and the cumulative reward in wrapper EvalEpisodeReturn are not the same.
            assert abs(timestep.info['eval_episode_return'].item() - eval_episode_return.item()) / \
                   abs(timestep.info['eval_episode_return'].item()) < 1e-5
            break