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Model-based Reinforcement Learning for Parameterized Action Spaces (2404.03037v3)

Published 3 Apr 2024 in cs.LG and cs.AI

Abstract: We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Decision Processes (PAMDPs). The agent learns a parameterized-action-conditioned dynamics model and plans with a modified Model Predictive Path Integral control. We theoretically quantify the difference between the generated trajectory and the optimal trajectory during planning in terms of the value they achieved through the lens of Lipschitz Continuity. Our empirical results on several standard benchmarks show that our algorithm achieves superior sample efficiency and asymptotic performance than state-of-the-art PAMDP methods.

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Authors (4)
  1. Renhao Zhang (2 papers)
  2. Haotian Fu (22 papers)
  3. Yilin Miao (1 paper)
  4. George Konidaris (71 papers)
Citations (2)
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Summary

Model-Based Reinforcement Learning for Parameterized Action Spaces

The paper "Model-based Reinforcement Learning for Parameterized Action Spaces" addresses the challenge of parameterized action Markov decision processes (PAMDPs). The complexity arises from the combination of discrete and continuous action spaces, which are prevalent in many practical applications such as robotics and real-time strategy games. To navigate this, the authors introduce Dynamics Learning and Predictive control with Parameterized Actions (DLPA), a model-based reinforcement learning (RL) method specifically tailored for PAMDPs.

Methodological Contributions

DLPA leverages the strengths of model-based RL to explore parameterized action spaces. Key innovations include:

  1. Parameterized Transition Model: Unlike prior model-free PAMDP methods, DLPA introduces a transition model capable of accommodating the entangled nature of parameterized actions. The authors propose three distinct inference structures to enhance model accuracy in capturing transition dynamics.
  2. H-step Prediction Loss: Instead of relying solely on single-step predictions, DLPA employs an H-step loss to train its transition models. This approach allows the model to better anticipate long-term outcomes by focusing on multi-step trajectories during the learning phase, thus decreasing compounding errors over time.
  3. Separate Reward Predictors: The method introduces dual reward predictors that differentiate between terminal and non-terminal states, reducing prediction errors that might arise from the termination conditions.
  4. PAMDP-specific Model Predictive Path Integral (MPPI): The proposed MPPI adapts the traditional method by maintaining separate distributions for continuous parameters associated with discrete actions, enhancing sampling efficiency and utilizing dependency between discrete and continuous components.

Theoretical Implications

The paper provides a theoretical framework to evaluate DLPA's performance guarantees. By employing Lipschitz continuity, it bridges the gap between theoretical guarantees and practical performance. The bounds derived highlight how various types of estimation errors influence overall performance, while future steps in planning methodologies can lower these errors in complex action spaces.

Empirical Evaluation

DLPA's evaluation, which included a range of PAMDP benchmarks, demonstrated significant improvements in both sample efficiency and asymptotic performance. Notably, DLPA achieved an average of 30 times higher sample efficiency compared to state-of-the-art model-free RL methods. Moreover, its ability to handle larger parameterized action spaces without learning complex action embeddings is a noteworthy practical advantage.

Future Directions

The results of this paper suggest promising avenues for future research in extending model-based RL to more sophisticated hierarchical action spaces and investigating further optimizations of planning algorithms tailored for PAMDPs. Another potential area of exploration lies in integrating DLPA with other burgeoning techniques in AI, such as meta-learning, to further enhance adaptability in unseen environments.

In conclusion, DLPA represents a substantive advancement in the field, providing a robust model-based framework that efficiently addresses the intricacies of PAMDPs. Its empirical success coupled with theoretical rigor lays the foundation for further exploration and application in varied decision-making domains.

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