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Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration

ICLR 2022 · Reinforcement learning › Offline RL · approach: Offline-to-online fine-tuning · 27 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add offline demonstration guidance with a decaying trust region to TRPO (LOGO)TRPO in sparse-reward MuJoCo tasks · MuJoCo sparse-reward environmentsrewardHelped
use TRPO without behavior-data guidanceTRPO · MuJoCo sparse-reward environmentsrewardHurt
imitate the behavior policy with GAIL instead of using online policy guidanceGAIL · MuJoCo sparse-reward environmentsrewardHurt
warm-start TRPO with behavior cloning, without ongoing online guidanceBC-TRPO · MuJoCo sparse-reward environmentsrewardMixed
use POfD to bootstrap learning from behavior data, retaining its influence throughout trainingPOfD · MuJoCo sparse-reward environmentsrewardHurt
use LOGO rather than DAPGLOGO and DAPG · MuJoCo sparse-reward environmentsrewardMixed
use LOGO with incomplete offline state observationsLOGO · MuJoCo sparse-reward environments with incomplete offline observationrewardMixed
use offline behavior-policy guidance with LOGOLOGO · TurtleBot waypoint trackingwaypoint-tracking performanceHelped
use sparse-reward TRPO without behavior-data guidanceTRPO · TurtleBot waypoint trackingwaypoint attainmentHurt
guide LOGO with waypoint behavior data that lacks obstacle information, while supplying the full state during learningLOGO · TurtleBot obstacle avoidanceobstacle-avoidance performanceHelped
use sparse-reward TRPO without behavior-data guidanceTRPO · TurtleBot obstacle avoidancecollision avoidanceHurt

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