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minor change so that SOMBRL agent does not get a list of rewards #1

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minor change so that SOMBRL agent does not get a list of rewards #1

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5fb603b
minor change so that SOMBRL agent does not get a list of rewards
klemensiten Jan 3, 2026
0160a41
add RewardLike class
klemensiten Jan 3, 2026
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1 change: 1 addition & 0 deletions ombrl/utils/multiple_reward_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ def __call__(self, observation, action, next_observation, reward):
def _get_reward(self, observation, action, next_observation, reward):
raise NotImplementedError(f'Reward function not set for reward_index: {self.reward_index}')

RewardLike = Callable | RewardFunction

class DmRewardFunction(RewardFunction):
def __call__(self, observation, action, next_observation, reward, env: DMCEnv):
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6 changes: 3 additions & 3 deletions ombrl/utils/train_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from jaxrl.datasets import ReplayBuffer
from maxinforl_jax.datasets import NstepReplayBuffer
from ombrl.utils.evaluation import evaluate
from ombrl.utils.multiple_reward_wrapper import RewardFunction
from ombrl.utils.multiple_reward_wrapper import RewardLike

from ombrl.utils.wrappers import PendulumInitWrapper
from ombrl.utils.env_utils import make_metaworld_env, make_humanoid_bench_env
Expand All @@ -28,7 +28,7 @@ def train(
alg_kwargs: Dict,
env_kwargs: Dict,
seed: int = 0,
reward_list: List[RewardFunction] | RewardFunction | None = None,
reward_list: Optional[List[RewardLike] | RewardLike] = None,
wandb_log: bool = True,
log_config: Optional[Dict] = None,
logs_dir: str = './logs',
Expand Down Expand Up @@ -118,7 +118,7 @@ def train(
reward_list, **alg_kwargs)
elif alg_name == 'sombrl':
if reward_list is not None:
assert isinstance(reward_list, RewardFunction), "Only one reward function can be passed to SOMBRL"
assert not isinstance(reward_list, List), "Only one reward function can be passed to SOMBRL"
agent = SOMBRLExplorerLearner(
seed,
env.observation_space.sample(),
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