Jul 25, 2023 · We suggest to take a graph perspective on the data an agent has collected and show that the structure of this data graph is linked to the degree of divergence ...

Jul 15, 2020 · In state of the art model-free off-policy deep reinforcement learning, a replay memory is used to store past experience and derive all network updates.

Using a neural network to express a parameterized set of nonlinear stable operators enables seamless integration with standard deep learning libraries. We ...

By using these lower bounds in TD learning, our method is less prone to soft divergence and exhibits increased sample efficiency while being more robust to.

The subgraph and its associated Q-values can be represented as a QGraph. We show that the Q-value for each transition in the simplified MDP is a lower bound of ...

Jul 15, 2020 · By using these lower bounds in temporal difference learning, our method QG-DDPG is less prone to soft divergence and exhibits increased sample ...

May 26, 2023 · DQNs combine deep learning with reinforcement learning to create agents capable of learning and making intelligent decisions in dynamic environments.

In this paper, we investigate causes of instability when using data augmentation in common off-policy RL algorithms. We identify two problems, both rooted in ...

An algorithm is developed which permits stable deep Q-learning for continuous control without any of the tricks conventionally used (such as target networks ...

Stabilizing deep Q-learning with Q-graph-based bounds. S Hoppe, M Giftthaler, R Krug, M Toussaint. The International Journal of Robotics Research 42 (9), 633 ...