Algorithms

When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
DOGE marries dataset geometry with deep function approximators in offline RL, and enables exploitation in generalizable OOD areas rather than strictly constraining policy within data distribution.
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When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
An Efficient Multi-Agent Optimization Approach for Coordinated Massive MIMO Beamforming
Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming …
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Mind the Gap: Offline Policy Optimization for Imperfect Rewards
This paper proposes an offline policy optimization approach for imperfect rewards. Abstract: Reward function is essential in …
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Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization
Offline reinforcement learning (RL) that learns policies from offline datasets without environment interaction has received …
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When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps, while also simultaneously allowing learning from a fixed real-world dataset.
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When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods …
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Discriminator-Guided Model-Based Offline Imitation Learning
Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward …
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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without …
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Model-Based Offline Planning with Trajectory Pruning
Offline reinforcement learning (RL) enables learning policies using pre-collected datasets without environment interaction, which …
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Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning
We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while …
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