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Hindsight Experience Replay (HER) is a technique used in reinforcementlearning (RL) that has proven to be very efficient for training off-policyRL-based agents to solve goal-based robotic manipulation tasks using sparserewards. Even though HER improves the sample efficiency of RL-based agents bylearning from mistakes made in past experiences, it does not provide anyguidance while exploring the environment. This leads to very large trainingtimes due to the volume of experience required to train an agent using thisreplay strategy. In this paper, we propose a method that uses primitivebehaviours that have been previously learned to solve simple tasks in order toguide the agent toward more rewarding actions during exploration while learningother more complex tasks. This guidance, however, is not executed by a manuallydesigned curriculum, but rather using a critic network to decide at eachtimestep whether or not to use the actions proposed by the previously-learnedprimitive policies. We evaluate our method by comparing its performance againstHER and other more efficient variations of this algorithm in several blockmanipulation tasks. We demonstrate the agents can learn a successful policyfaster when using our proposed method, both in terms of sample efficiency andcomputation time. Code is available at https://github.com/franroldans/qmp-her.

Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency