MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go. However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings. Viper, a recently proposed technique, constructs a decision tree policy by mimicking the DRL agent. Decision trees are interpretable as each action made can be traced back to the decision rule path that lead to it. However, one global decision tree approximating the DRL policy has significant limitations with respect to the geometry of decision boundaries. We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions. We propose a training procedure to support non-differentiable decision tree experts and integrate it into imitation learning procedure of Viper. We evaluate our algorithm on four OpenAI gym environments, and show that the policy constructed in such a way is more performant and better mimics the DRL agent by lowering mispredictions and increasing the reward. We also show that MoET policies are amenable for verification using off-the-shelf automated theorem provers such as Z3.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningGame of GoImitation LearningMixture-of-ExpertsOpenAI Gymreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method
RGBT tracking draws increasing attention due to its robustness in multi-modality warranting (MMW) scenarios, such as nighttime and bad weather, where relying on a single sensing modality fails to ensure stable tracking r…
Mixture-of-ExpertsRgb-T TrackingMoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm
Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real-world deployments often involve mixed d…
Test-time AdaptationMoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing
Mixture-of-Experts (MoE) model architecture has emerged as a promising solution for scaling transformer models efficiently, offering sparse activation that reduces computational costs while increasing model capacity. How…
GPUMixture-of-ExpertsSMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box policies that are effective, but suffer fr…
continuous-controlContinuous ControlMixture-of-ExpertsMuJoCoMoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, su…
Game of GoImitation LearningMixture-of-ExpertsOpenAI Gym+4