Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
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Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning
Neural Information Processing Systems
In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.
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Inverse Preference Learning: Preference-based RL without a Reward Function
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Reward functions are difficult to design and often hard to align with human intent.
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Appendices
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Here we present the proofs for the arguments behind each of the approximations described in Section4.
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WhatDidYouThinkWould Happen?Explaining AgentBehaviourthroughIntendedOutcomes
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This allows for local interpretation of the agent's intention based on its behavioural policy.
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