Does Learning Recurrent Networks Matter in Partially Observable RL?
Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent module trained jointly with the policy or value function.
ProofPaper ↗
Key points
- This raises a natural question: is learning the recurrent network actually necessary?
- In this paper, we investigate whether a frozen recurrent network can provide sufficient memory for solving par- tially observable Markov decision processes (POMDPs).
- Motivated by the theoretical foundations of Reservoir Computing (RC) and Generalized Reservoir Computing (GRC), we study a simple archi- tecture that combines a fixed recurrent network with a trainable nonlinear output module.
- Frozen recurrent networks therefore provide a simple, parameter-efficient, and sur- prisingly strong baseline for partially observable RL.
Sources (1)
- [1]Does Learning Recurrent Networks Matter in Partially Observable RL?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:17 AM
Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent module trained jointly with the policy or value function.
This raises a natural question: is learning the recurrent network actually necessary?
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