Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning
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Recent research introduces Feasibility-Guided Exploration (FGE), a method addressing the limitations of deep reinforcement learning in reachability problems. FGE identifies feasible initial conditions and learns a safe policy, outperforming existing methods by over 50% in coverage for challenging scenarios in the MuJoCo and Kinetix simulators. This approach enhances safety in high-dimensional control tasks.
Advancements in Reinforcement Learning Address Parameter-Robust Avoid Problems
Recent research has introduced a novel method, Feasibility-Guided Exploration (FGE), to enhance deep reinforcement learning (RL) in reachability problems. This method improves the effectiveness of RL in environments with unknown feasibility.
FGE identifies a subset of feasible initial conditions for establishing a safe policy while learning to solve the reachability problem in this subset. This dual approach allows for more comprehensive state space exploration.
Empirical Results
Empirical evaluations show FGE's superiority over existing methods. In experiments with the MuJoCo and Kinetix simulators, FGE achieved over 50% more coverage than competing approaches, highlighting its potential to enhance the robustness of RL frameworks in complex environments.
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📰 Original Source: https://arxiv.org/abs/2602.15817v1
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