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State2vec: Off-Policy Successor Features Approximators
[article]
2019
arXiv
pre-print
A major challenge in reinforcement learning (RL) is the design of 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
arXiv:1910.10277v1
fatcat:25jtnsjuqrc5va6w26ylsj5jc4