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Found 12 papers, 8 papers with code
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning.
Self-Organized Polynomial-Time Coordination Graphs
However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values.
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning.
On the Estimation Bias in Double Q-Learning
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.
Off-Policy Reinforcement Learning with Delayed Rewards
We study deep reinforcement learning (RL) algorithms with delayed rewards.
Generalizable Episodic Memory for Deep Reinforcement Learning
Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning.
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
Value decomposition is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings.
QPLEX: Duplex Dueling Multi-Agent Q-Learning
This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions.
Exploration via Hindsight Goal Generation
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space.
Object-Oriented Model Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability.
Object-Oriented Dynamics Learning through Multi-Level Abstraction
We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability.
