From 0dec89ff178bed3ef00c9054269c893c963a85f8 Mon Sep 17 00:00:00 2001
From: ottofabian <fabi.otto@t-online.de>
Date: Mon, 26 Jul 2021 17:07:45 +0200
Subject: [PATCH] Added basic test cases for functionality and determinism

---
 test/__init__.py  |   0
 test/test_envs.py | 108 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 108 insertions(+)
 create mode 100644 test/__init__.py
 create mode 100644 test/test_envs.py

diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_envs.py b/test/test_envs.py
new file mode 100644
index 0000000..addd1a3
--- /dev/null
+++ b/test/test_envs.py
@@ -0,0 +1,108 @@
+import unittest
+
+import gym
+import numpy as np
+
+import alr_envs  # noqa
+from alr_envs.utils.make_env_helpers import make_env
+
+ALL_SPECS = list(spec for spec in gym.envs.registry.all() if "alr_envs" in spec.entry_point)
+SEED = 1
+
+
+class TestEnvironments(unittest.TestCase):
+
+    def _run_env(self, env_id, iterations=None, seed=SEED, render=False):
+        """
+        Example for running a DMC based env in the step based setting.
+        The env_id has to be specified as `domain_name-task_name` or
+        for manipulation tasks as `manipulation-environment_name`
+
+        Args:
+            env_id: Either `domain_name-task_name` or `manipulation-environment_name`
+            iterations: Number of rollout steps to run
+            seed= random seeding
+            render: Render the episode
+
+        Returns:
+
+        """
+        env: gym.Env = make_env(env_id, seed=seed)
+        rewards = []
+        observations = []
+        dones = []
+        obs = env.reset()
+        self._verify_observations(obs, env.observation_space, "reset()")
+
+        length = env.spec.max_episode_steps
+        if iterations is None:
+            if length is None:
+                iterations = 1
+            else:
+                iterations = length
+
+        # number of samples(multiple environment steps)
+        for i in range(iterations):
+            observations.append(obs)
+
+            ac = env.action_space.sample()
+            # ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
+            obs, reward, done, info = env.step(ac)
+
+            self._verify_observations(obs, env.observation_space, "step()")
+            self._verify_reward(reward)
+            self._verify_done(done)
+
+            rewards.append(reward)
+            dones.append(done)
+
+            if render:
+                env.render("human")
+
+            if done:
+                obs = env.reset()
+
+        observations.append(obs)
+        env.close()
+        del env
+        return np.array(observations), np.array(rewards), np.array(dones)
+
+    def _verify_observations(self, obs, observation_space, obs_type="reset()"):
+        self.assertTrue(observation_space.contains(obs),
+                        f"Observation {obs} received from {obs_type} "
+                        f"not contained in observation space {observation_space}.")
+
+    def _verify_reward(self, reward):
+        self.assertIsInstance(reward, float, f"Returned {reward} as reward, expected float.")
+
+    def _verify_done(self, done):
+        self.assertIsInstance(done, bool, f"Returned {done} as done flag, expected bool.")
+
+    def test_environment_functionality(self):
+        """Tests that environments runs without errors using random actions."""
+        for spec in ALL_SPECS:
+            # try:
+            with self.subTest(msg=spec.id):
+                self._run_env(spec.id)
+
+    def test_environment_determinism(self):
+        """Tests that identical seeds produce identical trajectories."""
+        seed = 0
+        # Iterate over two trajectories generated using identical sequences of
+        # random actions, and with identical task random states. Check that the
+        # observations, rewards, discounts and step types are identical.
+        for spec in ALL_SPECS:
+            # try:
+            with self.subTest(msg=spec.id):
+                self._run_env(spec.id)
+                traj1 = self._run_env(spec.id, seed=seed)
+                traj2 = self._run_env(spec.id, seed=seed)
+                for i, time_step in enumerate(zip(*traj1, *traj2)):
+                    obs1, rwd1, done1, obs2, rwd2, done2 = time_step
+                    self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match.")
+                    self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
+                    self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
+
+
+if __name__ == '__main__':
+    unittest.main()