OpenSpiel is a collection of environments and algorithms for research in
general reinforcement learning and search/planning in games. OpenSpiel supports
n-player (single- and multi- agent) zero-sum, cooperative and general-sum,
Much human and computational effort has aimed to improve how deep
reinforcement learning algorithms perform on benchmarks such as the Atari
Learning Environment. Comparatively less effort has focused on understanding
what has been learned by such...
We study continuous action reinforcement learning problems in which it is
crucial that the agent interacts with the environment only through safe
policies, i.e.,~policies that do not take the agent to undesirable situations.
We formulate these...
Many reinforcement learning (RL) tasks provide the agent with
high-dimensional observations that can be simplified into low-dimensional
continuous states. To formalize this process, we introduce the concept of a
DeepMDP, a parameterized latent space...
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