แนวคิดหลัก
Adversarial Inverse Reinforcement Learning reevaluated for policy imitation and transferable reward recovery.
บทคัดย่อ
The content explores the reevaluation of Adversarial Inverse Reinforcement Learning (AIRL) from the perspectives of policy imitation and transferable reward recovery. It introduces a hybrid framework, PPO-AIRL + SAC, to address the limitations of SAC-AIRL in recovering transferable rewards. The analysis delves into the extractability of disentangled rewards by different policy optimization methods and environments. Various experiments are conducted to validate the performance of different algorithms in reward transfer scenarios.
- Introduction to Adversarial Inverse Reinforcement Learning (AIRL)
- Policy Imitation vs. Transferable Reward Recovery
- Hybrid Framework: PPO-AIRL + SAC
- Extractability of Disentangled Rewards by Different Methods
- Disentangled Condition on Environment Dynamics
- Reward Transferability Analysis with Experiments
สถิติ
"For both SAC-AIRL and PPO-AIRL, we train 1.5 × 106 steps in PointMaze (-Right, -Double) and 3 × 106 steps in Ant."
"PPO-AIRL requires extended training up to 5 × 106 steps."
"In Ant, SAC-AIRL is trained for 3 × 106 steps as in Section 4, while PPO-AIRL continues training until reaching 1 × 107 steps."
คำพูด
"Adversarial inverse reinforcement learning (AIRL) excels in learning disentangled rewards to maintain proper guidance through scenarios with changing dynamics."
"SAC-AIRL demonstrates a significant improvement in imitation performance but struggles with recovering transferable rewards."
"PPO-AIRL shows promise in recovering a disentangled reward when provided with a state-only ground truth reward."