This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an a…
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2 results for “contingency-aware”
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observationcs.LG
Paper tackles zero-shot policy learning for agents in new environments.
problem Learning in one environment and generalizing to unseen environments without additional data.
method Decomposes sparse rewards into spatial-temporal regions and uses Model Predictive Control (MPC).
result Method achieves natural generalization to new environments, demonstrated on various tasks.