Graph convolutional recurrent networks for reward shaping in reinforcement learning

of the environment is hard to compute, we use the Krylov basis to estimate the transition matrix, which outperforms the existing approximation bases. Unlike existing potential functions that only rely on states to perform reward shaping, we use both the states and actions through the look-ahead advice mechanism to produce more precise advice. Our evaluations conducted on the Atari 2600 and MuJoCo games show that our solution outperforms the state-of-the-art that utilizes GCN as the potential function in most games in terms of the learning speed while reaching higher rewards.