Unicorn: Continual Learning with a Universal, Off-policy Agent

Abstract

Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the agent's competence. In continual learning, also referred to as lifelong learning, there are no explicit task boundaries or curricula. As learning agents have become more powerful, continual learning remains one of the frontiers that has resisted quick progress. To test continual learning capabilities we consider a challenging 3D domain with an implicit sequence of tasks and sparse rewards. We propose a novel agent architecture called Unicorn, which demonstrates strong continual learning and outperforms several baseline agents on the proposed domain. The agent achieves this by jointly representing and learning multiple policies efficiently, using a parallel off-policy learning setup.

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Cited by in corpus (15)

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• Robust Reinforcement Learning for Continuous Control with Model Misspecification
• Hindsight policy gradients
• Online Continual Learning on Sequences
• Many-Goals Reinforcement Learning
• Universal Successor Features Approximators
• Multi-task Deep Reinforcement Learning with PopArt
• Deep Reinforcement and InfoMax Learning
• Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning
• Deep Sets for Generalization in RL
• Tokenized Data Markets
• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes