Mirrors_Framework/deepmind-research
1
0
Fork 0
mirror of https://github.com/google-deepmind/deepmind-research.git synced 2026-05-09 21:07:49 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Update introduction paragraph in README.md

Browse Source 
PiperOrigin-RevId: 290061509
This commit is contained in:
Victoria Krakovna
2020-01-16 15:20:56 +00:00
committed by Diego de Las Casas
parent d6385d6c13
commit 829d8444bb
1 changed files with 1 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files
side_effects_penalties/README.md
+1 -6
View File
@@ -1,11 +1,6 @@
# Side effects penalties
This is the code for the paper [Penalizing side effects using stepwise relative
reachability](https://arxiv.org/abs/1806.01186) by Krakovna et al (2019). It
implements a tabular Q-learning agent with different penalties for side effects.
Each side effects penalty consists of a deviation measure (none, unreachability,
relative reachability, or attainable utility) and a baseline (starting state,
inaction, or stepwise inaction).
Side effects are unnecessary disruptions to the agent's environment while completing a task. Instead of trying to explicitly penalize all possible side effects, we give the agent a general penalty for impacting the environment, defined as a deviation from some baseline state. For example, a reversibility penalty measures unreachability (deviation) of the starting state (baseline). This code implements a tabular Q-learning agent with different impact penalties. Each penalty consists of a deviation measure (none, unreachability, relative reachability, or attainable utility), a baseline (starting state, inaction, or stepwise inaction), and some other design choices. This is the code for the paper [Penalizing side effects using stepwise relative reachability](https://arxiv.org/abs/1806.01186) by Krakovna et al (2019).
## Instructions