“Playing Atari with Profound Support Learning” by Volodymyr Mnih et al. ( 2013) – This paper presented the Profound Q-Organization (DQN) calculation, which involved a brain organization to estimated the Q-capability in support getting the hang of, accomplishing human-level execution on a few Atari games.
“Offbeat Strategies for Profound Support Learning” by Volodymyr Mnih et al. ( 2016) – This paper proposed the Nonconcurrent Benefit Entertainer Pundit (A3C) calculation, which accomplished cutting edge results on a few Atari games and an assortment of constant control errands.
“Profound Support Learning for Mechanical technology” by Sergey Levine et al. ( 2016) – This paper exhibited the utilization of profound support learning for controlling mechanical frameworks, showing that it was equipped for figuring out how to play out different errands from crude tactile info.
“Dominating the Round of Dive with Deep Brain Organizations and Tree Search” by David Silver et al. ( 2016) – This paper presented AlphaGo, which utilized profound support learning and Monte Carlo tree search to overcome the best on the planet at the round of Go.
“Human-level control through profound support learning” by Volodymyr Mnih et al. ( 2015) – This paper showed the capacity of profound support figuring out how to figure out how to play a set-up of 49 Atari games at a godlike level.
“Trust District Strategy Advancement” by John Schulman et al. ( 2015) – This paper presented the Trust Area Strategy Improvement (TRPO) calculation, which accomplished cutting edge results on a few benchmark errands in mechanical technology and control.
“Persistent control with profound support learning” by Timothy Lillicrap et al. ( 2016) – This paper presented the Profound Deterministic Strategy Inclination (DDPG) calculation, which accomplished best in class results on a few nonstop control undertakings.
“Rainbow: Consolidating Enhancements in Profound Support Learning” by Matteo Hessel et al. ( 2018) – This paper presented the Rainbow calculation, which joined a few late enhancements in profound support figuring out how to accomplish best in class results on a few Atari games.
