In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD)
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

We explore unconstrained natural language feedback as a learning signal for artificial
agents. Humans use rich and varied language to teach, yet most prior work on interactive …

Dialogue policy plays an important role in task-oriented spoken dialogue systems. It
determines how to respond to users. The recently proposed deep reinforcement learning …

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain
dialogue policy. The rule-based policy is often reliable within predefined scope but not self …

Recently, deep reinforcement learning (DRL) has been used for dialogue policy
optimization. However, many DRL-based policies are not sample-efficient. Most recent …

Deep reinforcement learning has shown great potential in training dialogue policies.
However, its favorable performance comes at the cost of many rounds of interaction. Most of …

Dialogue policy learning based on reinforcement learning is difficult to be applied to real
users to train dialogue agents from scratch because of the high cost. User simulators, which …

Reinforcement learning (RL) has emerged as a key technique for designing dialogue
policies. However, action space inflation in dialogue tasks has led to a heavy decision …