In this paper, we investigate the exploration-exploitation dilemma of reinforcement learning algorithms. We adapt the information directed sampling, an exploration framework that measures the information gain of a policy, to the continuous reinforcement learning. To stabilize the off-policy learning process and further improve the sample efficiency, we propose to use a randomized learning target and to dynamically adjust the update-to-data ratio for different parts of the neural network model. Experiments show that our approach significantly improves over existing methods and successfully completes tasks with highly sparse reward signals.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we investigate the exploration-exploitation dilemma of reinforcement learning algorithms. We adapt the information directed sampling, an exploration framework that measures the information gain of a policy, to the continuous reinforcement learning. To stabilize the off-policy learning process and further improve the sample efficiency, we propose to use a randomized learning target and 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