Title: State2vec: Off-Policy Successor Feature Approximators. Abstract: A major challenge in reinforcement learning (RL) is how to design agents that are able to generalize across tasks that share common  dynamics. A viable solution is  meta-reinforcement learning,   which identifies common structures among past tasks to be then generalized to new tasks (meta-test). In meta-training, the RL agent learns state representations that encode  prior information from a set of  tasks, used to generalize the value function approximation. This has been proposed in the literature as   successor representation approximators. While promising, these methods do not generalize  well across optimal policies,  leading to sampling-inefficiency during   meta-test phases. In this paper, we propose state2vec, an efficient and low-complexity framework for learning  successor features which (i) generalize across policies, (ii) ensure sample-efficiency during meta-test. Representing each RL tasks as a graph, we extend the well known nod2vec framework to learn graph embeddings able to capture the discounted future state transitions in RL. The proposed off-policy state2vec captures the geometry of the underlying state space, making good basis functions for linear value function approximation. 