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Found 5 papers, 1 papers with code
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms.
Maximizing Ensemble Diversity in Deep Reinforcement Learning
In this paper, we describe Maximize Ensemble Diversity in Reinforcement Learning (MED-RL), a set of regularization methods inspired from the economics and consensus optimization to improve diversity in the ensemble-based deep reinforcement learning methods by encouraging inequality between the networks during training.
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning
With these techniques, we show that we can reduce communication by 75% with no loss of performance.
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity
Recently, the Maxmin and Ensemble Q-learning algorithms used the different estimates provided by ensembles of learners to reduce the bias.
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
