An empirical study shows deep RL algorithms struggle in continuing tasks without resets and that TD-based reward centering improves their performance across larger MuJoCo and Atari testbeds.
Pearl: A Production-ready Reinforcement Learning Agent
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performant RL policy requires a system designed to address several important challenges, including the exploration-exploitation dilemma, partial observability, dynamic action spaces, and safety concerns. While the importance of these challenges has been well recognized, existing open-source RL libraries do not explicitly address them. This paper introduces Pearl, a Production-Ready RL software package designed to embrace these challenges in a modular way. In addition to presenting benchmarking results, we also highlight examples of Pearl's ongoing industry adoption to demonstrate its advantages for production use cases. Pearl is open sourced on GitHub at github.com/facebookresearch/pearl and its official website is pearlagent.github.io.
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
An empirical study shows deep RL algorithms struggle in continuing tasks without resets and that TD-based reward centering improves their performance across larger MuJoCo and Atari testbeds.