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Reliant on too many experiments to learn good actions, current ReinforcementLearning (RL) algorithms have limited applicability in real-world settings,which can be too expensive to allow exploration. We propose an algorithm forbatch RL, where effective policies are learned using only a fixed offlinedataset instead of online interactions with the environment. The limited datain batch RL produces inherent uncertainty in value estimates of states/actionsthat were insufficiently represented in the training data. This leads toparticularly severe extrapolation when our candidate policies diverge from onethat generated the data. We propose to mitigate this issue via twostraightforward penalties: a policy-constraint to reduce this divergence and avalue-constraint that discourages overly optimistic estimates. Over acomprehensive set of 32 continuous-action batch RL benchmarks, our approachcompares favorably to state-of-the-art methods, regardless of how the offlinedata were collected.

Continuous Doubly Constrained Batch Reinforcement Learning