arXiv:2202.11960
공식 코드 · Papers with Code
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
원문 초록
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. UDRL is based purely on supervised learning, and bypasses some prominent issues in RL: bootstrapping, off-policy corrections, and discount factors. While previous work with UDRL demonstrated it in a traditional online RL setting, here we show that this single algorithm can also work in the imitation learning and offline RL settings, be extended to the goal-conditioned RL setting, and even the meta-RL setting. With a general agent architecture, a single UDRL agent can learn across all paradigms.
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Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMs
토론
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