Model-based reinforcement learning under concurrent schedules of reinforcement in rodents

Assessing Animals’ Preferences: Concurrent Schedules of Reinforcement

Reinforcement Learning under Model Mismatch

We study reinforcement learning under model misspecification, where we do not have access to the true environment but only to a reasonably close approximation to it. We address this problem by extending the framework of robust MDPs of [2, 17, 13] to themodel-free Reinforcement Learning setting, where we do not have access to the model parameters, but can only sample states from it. We define ro...

Concurrent Hierarchical Reinforcement Learning

We describe a language for partially specifying policies in domains consisting of multiple subagents working together to maximize a common reward function. The language extends ALisp with constructs for concurrency and dynamic assignment of subagents to tasks. During learning, the subagents learn a distributed representation of the Q-function for this partial policy. They then coordinate at run...

Reinforcement Learning: Model-based

Reinforcement learning (RL) refers to a wide range of dierent learning algorithms for improving a behavioral policy on the basis of numerical reward signals that serve as feedback. In its basic form, reinforcement learning bears striking resemblance to ‘operant conditioning’ in psychology and animal learning: actions that are rewarded tend to occur more frequently; actions that are punished ar...

Model-Based Reinforcement Learning

Reinforcement Learning (RL) refers to learning to behave optimally in a stochastic environment by taking actions and receiving rewards [1]. The environment is assumed Markovian in that there is a fixed probability of the next state given the current state and the agent’s action. The agent also receives an immediate reward based on the current state and the action. Models of the next-state distr...