Scallable partially observable and/or non-Markovian gridworld for planning or reinforcement learning
Codebase of ηψ-Learning algorithm that learns a non-Markovian maximum state entropy exploration policy by combining predecessor and successor representation to estimate the state visitation distribution of a trajectory of finite length.
Solving the problem of non-Markovian reward functions by providing agents access to a finite amount of memory
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