Added test cases for start value

Added tests to the pylint checks to confirm the correct action bounds of predict and step method

tests/test_spaces.py

@@ -53,6 +53,15 @@ def __init__(self, nvec):
             BOX_SPACE_FLOAT32,
         )
 
+class ActionBoundsTestClass(DummyEnv):
+    def step(self, action):
+        if isinstance(self.action_space, spaces.Discrete):
+            assert np.all(action >= self.action_space.start), f"Discrete action {action} is below the lower bound {self.action_space.start}"
+            assert np.all(action <= self.action_space.start + self.action_space.n), f"Discrete action {action} is above the upper bound {self.action_space.start}+{self.action_space.n}"
+        elif isinstance(self.action_space, spaces.Box):
+            assert np.all(action >= self.action_space.low), f"Action {action} is below the lower bound {self.action_space.low}"
+            assert np.all(action <= self.action_space.high), f"Action {action} is above the upper bound {self.action_space.high}"
+        return self.observation_space.sample(), 0.0, False, False, {}
 
 @pytest.mark.parametrize(
     "env",
@@ -170,6 +179,44 @@ def test_float64_action_space(model_class, obs_space, action_space):
     assert action.dtype == env.action_space.dtype
 
 
+@pytest.mark.parametrize(
+        "model_class, action_space",
+        [
+            # on-policy test
+            (PPO, spaces.Discrete(5, start=-6543)),
+            (PPO, spaces.Box(low=2344, high=2345, shape=(3,), dtype=np.float32)),
+            # off-policy test
+            (DQN, spaces.Discrete(2, start=9923)),
+            (SAC, spaces.Box(low=-123, high=-122, shape=(1,), dtype=np.float32)),
+        ],
+    )
+def test_space_bounds(model_class, action_space):
+    obs_space = BOX_SPACE_FLOAT32
+    env = ActionBoundsTestClass(obs_space, action_space)
+    env = gym.wrappers.TimeLimit(env, max_episode_steps=200)
+    if isinstance(env.observation_space, spaces.Dict):
+        policy = "MultiInputPolicy"
+    else:
+        policy = "MlpPolicy"
+
+    if model_class in [PPO, A2C]:
+        kwargs = dict(n_steps=64, policy_kwargs=dict(net_arch=[12]))
+    else:
+        kwargs = dict(learning_starts=60, policy_kwargs=dict(net_arch=[12]))
+
+    model = model_class(policy, env, **kwargs)
+    model.learn(64)
+    initial_obs, _ = env.reset()
+
+    action, _ = model.predict(initial_obs, deterministic=False)
+    if isinstance(action_space, spaces.Discrete):
+        assert np.all(action >= action_space.start), f"Discrete action {action} is below the lower bound {action_space.start}"
+        assert np.all(action <= action_space.start + action_space.n), f"Discrete action {action} is above the upper bound {action_space.start}+{action_space.n}"
+    elif isinstance(action_space, spaces.Box):
+        assert np.all(action >= action_space.low), f"Action {action} is below the lower bound {action_space.low}"
+        assert np.all(action <= action_space.high), f"Action {action} is above the upper bound {action_space.high}"
+
+
 def test_multidim_binary_not_supported():
     env = DummyEnv(BOX_SPACE_FLOAT32, spaces.MultiBinary([2, 3]))
     with pytest.raises(AssertionError, match=r"Multi-dimensional MultiBinary\(.*\) action space is not supported"):