Search Results for author:
Found 3 papers, 0 papers with code
Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy
Most of the existing dialogue policy methods rely on a single learning system, while the human brain has two specialized learning and memory systems, supporting to find good solutions without requiring copious examples.
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization
When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn.
Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning
We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning.
