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Can a reinforcement learning agent practice before it starts learning?

A reinforcement learning (RL) agent needs a fair amount of experience to find a near-optimal policy. Transfer learning has been investigated as a means to reduce the amount of experience required. Transfer learning, however, requires another similar reinforcement learning task as a transfer source, which can also be costly in the amount of experience required. In this research, we examine the possible “practice” approach that transfers knowledge from a non-RL task to a target RL task to avoid the expensive data sampling. We analyze how practice captures the distributions of state and action spaces in an environment. For this, we develop a novel learning approach that acquires important samples from practice and then applies them to a target RL task without changing learned bases. Results show an improved learning efficiency through practice in classical benchmark problems and limitations in OpenAI Gym problems.