In MLIR test_ppo_train_smoke make wrapper env compilant with Gym
@@ -273,10 +273,10 @@ def test_ppo_train_smoke():
random.seed(seed)
torch.manual_seed(seed)
register_env(
- "mlir_env", lambda env_config: MlirRlWrapperEnv(env=gym.make("mlir-v0"))
+ "mlir_rl_env-v0", lambda env_config: MlirRlWrapperEnv(env=gym.make("mlir-v0"))
)
config = {
- "env": "mlir_env",
+ "env": "mlir_rl_env-v0",
"framework": "torch",
# Tweak the default model provided automatically by RLlib,
# given the environment's observation- and action spaces.
