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Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning
Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning.
Improving Interactive Reinforcement Agent Planning with Human Demonstration
Our results show that learning from demonstration can allow a TAMER agent to learn a roughly optimal policy up to the deepest search and encourage the agent to explore along the optimal path.
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
In this paper, we proposed a deep interactive reinforcement learning method for path following of AUV by combining the advantages of deep reinforcement learning and interactive RL.
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user.
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback
Deep reinforcement learning (DRL) has achieved great successes in many simulated tasks.
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
Multi-agent generative adversarial imitation learning (MAGAIL) allows multi-AUV to learn from expert demonstration instead of pre-defined reward functions, but suffers from the deficiency of requiring optimal demonstrations and not surpassing provided expert demonstrations.
