PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies

• URL: http://arxiv.org/abs/2502.11377v1
• Date: Mon, 17 Feb 2025 02:46:02 GMT
• Title: PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies
• Authors: Morgan Byrd, Jackson Crandell, Mili Das, Jessica Inman, Robert Wright, Sehoon Ha,
• Abstract summary: We introduce PrivilegedDreamer, a model-based reinforcement learning framework that extends the existing model-based approach by incorporating an explicit parameter estimation module. Our empirical analysis on five diverse HIP-MDP tasks demonstrates that PrivilegedDreamer outperforms state-of-the-art model-based, model-free, and do- main adaptation learning algorithms.
• Score: 7.376615925443845
• License:
• Abstract: Numerous real-world control problems involve dynamics and objectives affected by unobservable hidden pa- rameters, ranging from autonomous driving to robotic manipu- lation, which cause performance degradation during sim-to-real transfer. To represent these kinds of domains, we adopt hidden- parameter Markov decision processes (HIP-MDPs), which model sequential decision problems where hidden variables parameterize transition and reward functions. Existing ap- proaches, such as domain randomization, domain adaptation, and meta-learning, simply treat the effect of hidden param- eters as additional variance and often struggle to effectively handle HIP-MDP problems, especially when the rewards are parameterized by hidden variables. We introduce Privileged- Dreamer, a model-based reinforcement learning framework that extends the existing model-based approach by incorporating an explicit parameter estimation module. PrivilegedDreamer features its novel dual recurrent architecture that explicitly estimates hidden parameters from limited historical data and enables us to condition the model, actor, and critic networks on these estimated parameters. Our empirical analysis on five diverse HIP-MDP tasks demonstrates that PrivilegedDreamer outperforms state-of-the-art model-based, model-free, and do- main adaptation learning algorithms. Additionally, we conduct ablation studies to justify the inclusion of each component in the proposed architecture.

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