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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Adaptive Online Replanning with Diffusion Models Siyuan Zhou
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Given a previously generated plan, how do we effectively regenerate a new plan?
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Weakly Coupled Deep Q-Networks
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"subagents," one for each subproblem, and then combine their solutions to establish
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ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
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Human beings are able to transfer the policies swiftly to a structurally similar task.
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Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
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The circles and crosses represent the object and goal positions, respectively.
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Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents
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This approach seeks to modify other agents'
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