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Imitating Graph-Based Planning with Goal-Conditioned Policies

ICLR 2023 · Reinforcement learning › Visual and representation RL · approach: Graph search and compositional goals · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add PIG self-imitation and subgoal skippingMSS · Large U-shaped AntMazesuccess rate: 19.08% -> 57.41%Helped
Add planning-guided self-imitationMSS · Long-horizon continuous control taskssample-efficiencyHelped
Train with PIG and remove the planner at test time (ablation)MSS + PIG · U-shaped AntMazesuccess rateHelped
Replace PIG self-imitation with hindsight action imitation (ablation)MSS + PIG without subgoal skipping · 2DReach, Pusher, L-shaped AntMazesample-efficiencyHurt
Add subgoal skipping (ablation)PIG · 2DReach and Pushersuccess rateHelped
Add subgoal skipping (ablation)PIG · L-shaped AntMazesuccess rateNo clear effect
Add subgoal skipping (ablation)PIG · Reacher and Large U-shaped AntMazesuccess rateMixed
Use random subgoal selection instead of loss-conditioned skipping (ablation)PIG · 2DReachsuccess rateNo clear effect
Use random subgoal selection instead of loss-conditioned skipping (ablation)PIG · Pushersuccess rateHurt
Set the self-imitation loss coefficient to 1e-3 or 1e-4 rather than 0 (ablation)PIG · L-shaped AntMazesuccess rateHelped
Use too large a self-imitation loss coefficient (ablation)PIG · L-shaped AntMazesuccess rateHurt
Use subgoal skipping (ablation)PIG · 2DReachstate entropyHelped

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