We present RLCard, a Python platform for reinforcement learning research and development in card games. RLCard supports various card environments and several baseline algorithms with unified easy-to-use interfaces, aiming at bridging reinforcement learning and imperfect information games. The platform provides flexible configurations of state representation, action encoding, and reward design. RLCard also supports visualizations for algorithm debugging. In this demo, we showcase two representative environments and their visualization results. We conclude this demo with challenges and research opportunities brought by RLCard. A video is available on YouTube.

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A multiple transmit antenna, single receive antenna (per receiver) downlink channel with limited channel feedback is considered. Given a constraint on the total system-wide channel feedback, the following question is considered: is it preferable to get low-rate feedback from a large number of receivers or to receive high-rate/high-quality feedback from a smaller number of (randomly selected) receivers. Acquiring feedback from many users allows multi-user diversity to be exploited, while highrate feedback allows for very precise selection of beamforming directions. It is shown that systems in which a limited number of users feedback high-rate channel information significantly outperform low-rate/many user systems. The marginal benefit of channel feedback is very significant up to the point where the CSI is essentially perfect.

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