A new training approach for robot navigation allows multiple collisions per episode before reset, accelerating early learning and improving success rates over traditional single-collision resets.
Recovery rl: Safe reinforcement learning with learned recovery zones,
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A separate regulator module adaptively scales actions in RL to reduce constraint violations while preserving exploration, yielding up to 126x fewer violations and over 10x higher returns on Safety Gym tasks.
citing papers explorer
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Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
A new training approach for robot navigation allows multiple collisions per episode before reset, accelerating early learning and improving success rates over traditional single-collision resets.
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Constraint-Aware Reinforcement Learning via Adaptive Action Scaling
A separate regulator module adaptively scales actions in RL to reduce constraint violations while preserving exploration, yielding up to 126x fewer violations and over 10x higher returns on Safety Gym tasks.