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Found 8 papers, 4 papers with code
No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning
The large action space is one fundamental obstacle to deploying Reinforcement Learning methods in the real world.
Learning Diverse Risk Preferences in Population-based Self-play
In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations.
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
We show that such lossless primitives can drastically improve the performance of hierarchical policies.
On the Role of Discount Factor in Offline Reinforcement Learning
The discount factor, $\gamma$, plays a vital role in improving online RL sample efficiency and estimation accuracy, but the role of the discount factor in offline RL is not well explored.
Offline Reinforcement Learning with Value-based Episodic Memory
Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks.
