Sparse Reward Processes

Asymptotic Behavior of Multivariate Reward Processes with Nonlinear Reward Functions

COVARIANCE MATRIX OF MULTIVARIATE REWARD PROCESSES WITH NONLINEAR REWARD FUNCTIONS

Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...

Reward Shaping for Statistical Optimisation of Dialogue Management

This paper investigates the impact of reward shaping on a reinforcement learning-based spoken dialogue system’s learning. A diffuse reward function gives a reward after each transition between two dialogue states. A sparse function only gives a reward at the end of the dialogue. Reward shaping consists of learning a diffuse function without modifying the optimal policy compared to a sparse one....

Loss is its own Reward: Self-Supervision for Reinforcement Learning

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of selfsupervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquito...

Self-Supervision for Reinforcement Learning

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquit...