Unity-Technologies/ml-agents · error · UnityGymException

The gym wrapper does not provide explicit support for both d

Error message

The gym wrapper does not provide explicit support for both discrete and continuous actions.

What it means

UnityGymException thrown by UnityGymEnv.__init__ when the wrapped behavior's action_spec has both nonzero continuous_size and nonzero discrete branches. The gym wrapper only supports one action modality at a time, so it refuses hybrid (discrete+continuous) action spaces.

Source

Thrown at ml-agents-envs/mlagents_envs/envs/unity_gym_env.py:129

            else:
                if flatten_branched:
                    self._flattener = ActionFlattener(branches)
                    self._action_space = self._flattener.action_space
                else:
                    self._action_space = spaces.MultiDiscrete(branches)

        elif self.group_spec.action_spec.is_continuous():
            if flatten_branched:
                logger.warning(
                    "The environment has a non-discrete action space. It will "
                    "not be flattened."
                )

            self.action_size = self.group_spec.action_spec.continuous_size
            high = np.array([1] * self.group_spec.action_spec.continuous_size)
            self._action_space = spaces.Box(-high, high, dtype=np.float32)
        else:
            raise UnityGymException(
                "The gym wrapper does not provide explicit support for both discrete "
                "and continuous actions."
            )

        if action_space_seed is not None:
            self._action_space.seed(action_space_seed)

        # Set observations space
        list_spaces: List[gym.Space] = []
        shapes = self._get_vis_obs_shape()
        for shape in shapes:
            if uint8_visual:
                list_spaces.append(spaces.Box(0, 255, dtype=np.uint8, shape=shape))
            else:
                list_spaces.append(spaces.Box(0, 1, dtype=np.float32, shape=shape))
        if self._get_vec_obs_size() > 0:
            # vector observation is last
            high = np.array([np.inf] * self._get_vec_obs_size())

View on GitHub (pinned to 3ecb446f75)

Solutions

1. In Unity Behavior Parameters, set one modality to zero: either Continuous Actions = 0 or remove all discrete branches, then rebuild.
2. Set flatten_branches/use_discrete handling consistent with the spec — only discrete-only or continuous-only environments can be wrapped.
3. Use the raw UnityEnvironment API (behavior_specs action_spec gives continuous and discrete parts) for hybrid control.
4. Report/extend the wrapper if hybrid gym support is required; upstream gym wrapper doesn't support it.

Example fix

// before
# Unity Behavior Parameters: Continuous Actions = 3, Discrete branches = [2,2]
env = UnityGymEx(UnityEnvironment(file_name='hybrid_app'))
// after
# Unity Behavior Parameters: Continuous Actions = 0, Discrete branches = [2,2]
env = UnityGymEnv(UnityEnvironment(file_name='discrete_app'))

spec = unity_env.behavior_specs[behavior_name]
aspec = spec.action_spec
if aspec.continuous_size > 0 and len(aspec.discrete_branches) > 0:
    raise ValueError("Hybrid actions not supported by the gym wrapper")

def is_single_modality(action_spec) -> bool:
    return (action_spec.continuous_size > 0) != (len(action_spec.discrete_branches) > 0)

try:
    env = UnityGymEnv(unity_env)
except UnityGymException:
    logger.error("Hybrid action space; set exactly one of continuous/discrete in Behavior Parameters")

When it happens

Trigger: Wrapping an environment whose Behavior Parameters in Unity enable both a continuous action vector and discrete action branches (Continuous Actions > 0 AND Discrete Actions branches defined).

Common situations: ML-Agents project configured with hybrid actions for research; recently edited Behavior Parameters adding discrete branches to a previously continuous controller; trying to wrap ML-Agents 1.0+ hybrid-action environments in gym.

Related errors

• There can only be one behavior in a UnityEnvironment if it i
• There are no observations provided by the environment.
• There can only be one Agent in the environment but {n_agents
• Action spaces with both continuous and discrete actions are
• The behavior {name} needs a continuous input of dimension {_

AI-assisted analysis of Unity-Technologies/ml-agents@3ecb446f75 (2026-09-02). Data as JSON: /api/errors/883d5c160bbec83f. Report an issue: GitHub.