RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with online data collection in the real-world, including cost, safety, or ethical concerns... (read more)

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Methods used in the Paper


METHOD TYPE
Q-Learning
Off-Policy TD Control
DQN
Q-Learning Networks
REM
Q-Learning Networks