Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game ...
In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mechanisms that lead to such rapid ...
Data on human learning trajectories for several Atari games is presented, and several hypotheses about the mechanisms that lead to such rapid learning are ...
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We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.
In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to ...
Feb 25, 2015 · An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, ...
Mar 31, 2020 · The main improvement in this paper is learning to manage exploration versus exploitation. This is particularly important for performing well in tasks that don' ...
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scal-.
Jun 26, 2018 · In the human start evaluation, learned agents begin episodes of randomly sampled point from a human professional's game-play. My question is:
Feb 17, 2021 · al (2021) use a deep reinforcement learning algorithm to understand human neural activation evoked by playing different video games. The ...