Title: Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning. Abstract: In state of the art model-free off-policy deep reinforcement learning (RL), a replay memory is used to store past experience and derive all network updates. Even if both state and action spaces are continuous, the replay memory only holds a finite number of transitions.  We represent these transitions in a data graph and link its structure to soft divergence. By selecting a subgraph with a favorable structure, we construct a simple Markov Decision Process (MDP) for which exact Q-values can be computed efficiently as more data comes in - resulting in a Qgraph. We show that the Q-value for each transition in the simplified MDP is a lower bound of the Q-value for the same transition in the original continuous Q-learning problem. 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 hyperparameters. Qgraphs also retain information from transitions that have already been overwritten in the replay memory, which can decrease the algorithm's sensitivity to the replay memory capacity.
