Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation: An Enhanced Approach for Dynamic Environments
The authors propose IDEM, a novel method that enhances Deep Q-Networks (DQN) to adapt to dynamic environments by dynamically adjusting experience replay weights and learning rates based on real-time feedback, leading to improved performance and stability in unpredictable settings.
Simplifying Deep Temporal Difference Learning with LayerNorm and Parallelized Q-Learning (PQN)
This paper argues that LayerNorm with L2 regularization can stabilize off-policy Temporal Difference (TD) learning, eliminating the need for target networks and replay buffers, and proposes PQN, a simplified deep Q-learning algorithm that leverages parallelized environments for efficient and stable training.
Adaptive Q-Network: On-the-Fly Target Selection for Deep Reinforcement Learning (Under review as a conference paper at ICLR 2025)
Adaptive Q-Network (AdaQN) improves deep reinforcement learning by dynamically selecting the best-performing hyperparameters during training, leading to faster learning, better performance, and increased robustness compared to traditional static hyperparameter approaches and existing AutoRL methods.
Improving Data Efficiency in Deep Reinforcement Learning Control: Generalized Policy Improvement Algorithms with Sample Reuse
This paper introduces Generalized Policy Improvement (GPI) algorithms, a novel class of deep reinforcement learning algorithms that enhance data efficiency by safely reusing samples from recent policies while preserving the performance guarantees of on-policy methods.
Deep Convolutional Q-Networks Outperform Deep Transformer Q-Networks in Most Atari Games
While Deep Transformer Q-Networks (DTQNs) show promise in leveraging sequential data for reinforcement learning, Deep Convolutional Q-Networks (DCQNs) currently demonstrate superior performance in terms of speed and average reward across a variety of Atari games, except for specific game environments where DTQNs can exploit predictable patterns.
Deep Exploration in Continuous Control Tasks with Sparse Rewards Using a PAC-Bayesian Actor-Critic Algorithm
This research paper introduces PBAC, a novel PAC-Bayesian actor-critic algorithm designed for deep exploration in continuous control tasks with sparse rewards, demonstrating superior performance compared to existing methods.
Deep Reinforcement Learning zur Lösung von Managementproblemen: Auf dem Weg zu einem großen Managementmodell
Ein DRL-Ansatz revolutioniert das Management durch ein großes Managementmodell.
© 2024 by Linnk AI