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Transferring Domain Knowledge with an Adviser in Continuous Tasks

arXiv 2102.08029 ↗First posted 16 Feb 2021cs.AIcited by 0

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Abstract (arXiv)

Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapable of explicitly incorporating already known domain-specific knowledge into the learning process. Therefore, the agents have to explore and learn the domain knowledge independently through a trial and error approach, which consumes both time and resources to make valid responses. Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to incorporate an adviser, which allows integrating domain knowledge in the form of pre-learned policies or pre-defined relationships to enhance the agent's learning process. Our experiments on OpenAi Gym benchmark tasks show that integrating domain knowledge through advisers expedites the learning and improves the policy towards better optima.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 16 Feb 2021.

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References · 17

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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5 more
  1. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 86
  2. 2017Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigationcited by 74
  3. 2018Unsupervised Predictive Memory in a Goal-Directed Agentcited by 54
  4. 2016Deep Reinforcement Learning with Successor Features for Navigation across Similar Environmentscited by 50
  5. 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 25

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