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TGRL: An Algorithm for Teacher Guided Reinforcement Learning

ICML 2023 · Reinforcement learning › Visual and representation RL · approach: In-context and adaptive RL · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Adding the teacher-guidance term with adaptive coefficient (TGRL) instead of pure imitation learningstudent policy on Shadow Hand tactile pen reorientation · Shadow Hand pen reorientation (tactile)success rate: 54% -> 73%Helped
Using a sub-optimal teacher (0% success) vs perfect teacher for TGRL student (ablation)TGRL student on Tiger Door · Tiger Doorstudent success rate: 100% with teachers at 100/80/40% but 0% with a 0% teacherMixed
Lowering teacher quality from 100% to 0% success (ablation)TGRL student on Lava Crossing · Lava Crossingstudent success rate: 100% -> 8%Hurt
Joint replay buffer shared between main policy and auxiliary policy (vs separate buffers) (ablation)TGRL on Light-Dark Ant · Light-Dark Antconvergence rateHelped
Adaptive balancing coefficient (dual update of lambda) instead of fixed coefficient (ablation)TGRL on Shadow Hand · Shadow Hand pen reorientation (tactile)asymptotic successHelped
Using a fixed balancing coefficient (including the best-found value) instead of adaptive coefficient (ablation)TGRL · fixed-coefficient sweepfinal performanceHurt
Adaptive coefficient vs constant alpha across initial values in Lava Crossing (ablation)TGRL · Lava Crossingconvergence to optimal policyHelped
Replacing TGRL with ADVISOR-off baselineADVISOR baseline · Tiger Door and Lava Crossingsuccess rateHurt
Using TGRL with mostly fixed hyperparameters (no per-task tuning) vs COSIL with 8-value hyperparameter searchCOSIL baseline average across hyperparameters · Imitation Gap domainssuccess rateHurt
Adding TGRL on domains without Imitation Gap (proprioceptive Hopper, Walker2D, HalfCheetah with joint positions only)TGRL vs IL · POMDP Mujoco without Imitation Gapreturn vs teacherNo clear effect

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