atlas

Research map / Reinforcement learning / Visual and representation RL

Advice-Guided Reinforcement Learning in a non-Markovian Environment

AAAI 2021 · Reinforcement learning › Visual and representation RL · approach: In-context and adaptive RL · 26 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add advice {b} instead of using no advice (ablation)AdvisoRL · Officeworldtraining steps to convergence: 454.5 -> 307.5Helped
Add advice {bd} instead of using no advice (ablation)AdvisoRL · Officeworldtraining steps to convergence: 454.5 -> 189.0Helped
Add advice {bdb} instead of using no advice (ablation)AdvisoRL · Officeworldtraining steps to convergence: 454.5 -> 144.0Helped
Add advice {bdbg} instead of using no advice (ablation)AdvisoRL · Officeworldtraining steps to convergence: 454.5 -> 142.5Helped
Add advice {bdbg} instead of using no advice (ablation)AdvisoRL · Officeworldinference time: 149.0 -> 0.5Helped
Provide advice {g} alone rather than no advice (ablation)AdvisoRL · Officeworldcumulative inference timeHurt
Provide advice {g} alone rather than no advice (ablation)AdvisoRL · Officeworldtraining steps to convergenceHelped
Add advice {a} to guide reward-machine inference (ablation)AdvisoRL · Taxiattained rewardHelped
Add advice {eabf} to guide reward-machine inference (ablation)AdvisoRL · Taxiattained rewardHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy