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Found 3 papers, 1 papers with code
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
Exploration via Flow-Based Intrinsic Rewards
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
Mixture of Step Returns in Bootstrapped DQN
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
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