Direct Behavior Specification via Constrained Reinforcement Learning

Abstract

The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. Most often, practitioners go about the task of behavior specification by manually engineering the reward function, a counter-intuitive process that requires several iterations and is prone to reward hacking by the agent. In this work, we argue that constrained RL, which has almost exclusively been used for safe RL, also has the potential to significantly reduce the amount of work spent for reward specification in applied Reinforcement Learning projects. To this end, we propose to specify behavioral preferences in the CMDP framework and to use Lagrangian methods, which seek to solve a min-max problem between the agent's policy and the Lagrangian multipliers, to automatically weigh each of the behavioral constraints. Specifically, we investigate how CMDPs can be adapted in order to solve goal-based tasks while adhering to a set of behavioral constraints and propose modifications to the SAC-Lagrangian algorithm to handle the challenging case of several constraints. We evaluate this framework on a set of continuous control tasks relevant to the application of Reinforcement Learning for NPC design in video games.

Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance

“…25, where λ is a Lagrange multiplier. In our experiments, we fix λ to be a constant scalar, although adaptive approaches such as (Roy et al, 2021) could be explored.…”