แนวคิดหลัก
In some scenarios, feasible policies should be discontinuous or multi-valued to avoid constraint violations, challenging the assumption of continuous policies in safe reinforcement learning.
บทคัดย่อ
The article explores the necessity of policy bifurcation in safe reinforcement learning, highlighting the limitations of continuous policies. It introduces the concept of multimodal policy optimization (MUPO) using Gaussian mixture distributions to achieve bifurcated policies. The study reveals theoretical insights and experimental validations showcasing the superiority of bifurcated policies over continuous ones in ensuring safety and optimality in complex control tasks.
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Introduction
- Safe reinforcement learning (RL) addresses constrained optimal control problems.
- Existing studies assume continuity in policy functions but fail to consider scenarios where discontinuous policies are necessary.
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Core Concepts
- Continuous vs. Discontinuous Policies: The need for abrupt changes in actions based on states.
- Topological Analysis: Contractibility and non-simply connected constraints.
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Experimental Validation
- Simulation Experiments: MUPO algorithm outperforms DSAC and SAC in vehicle control tasks.
- Real-world Experiments: Demonstrated that only bifurcated policies ensure safety under varying conditions.
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Theoretical Framework
- Lemmas and Theorems: Suboptimality and Infeasibility of Continuous Policies under specific conditions.
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Bifurcated Policy Construction
- Gaussian Mixture Distribution: Utilized to create stochastic policies with abrupt action changes.
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MUPO Algorithm
- Actor-Critic Architecture: Incorporates DSAC for comprehensive action-value distribution evaluation.
- Policy Evaluation: Rewards modified with penalty function to handle state constraints effectively.
สถิติ
Existing research overlooks a serious issue: In many cases, no feasible continuous policy solution may exist for constrained OCPs.
For a constrained OCP characterized by a Lipschitz continuous dynamic function f and policy π, if the optimal solution corresponds to a reachable tuple R that is noncontractible, then the optimal solution cannot be achieved by continuous policy.
คำพูด
"In such scenarios, feasible policies should be bifurcated."
"Our theorem reveals that a feasible policy is required to be bifurcated."