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This paper introduces two learning schemes for distributed agents inReinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted)and Loss-Weighted (L-Weighted) gradient merger. The R/L weighted methodsreplace standard practices for training multiple agents, such as summing oraveraging the gradients. The core of our methods is to scale the gradient ofeach actor based on how high the reward (for R-Weighted) or the loss (forL-Weighted) is compared to the other actors. During training, each agentoperates in differently initialized versions of the same environment, whichgives different gradients from different actors. In essence, the R-Weights andL-Weights of each agent inform the other agents of its potential, which againreports which environment should be prioritized for learning. This approach ofdistributed learning is possible because environments that yield higherrewards, or low losses, have more critical information than environments thatyield lower rewards or higher losses. We empirically demonstrate that theR-Weighted methods work superior to the state-of-the-art in multiple RLenvironments.

Loss and Reward Weighing for increased learning in Distributed Reinforcement Learning