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Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Pei-Hao Su, Pawel Budzianowski, Stefan Ultes, Milica Gasic, Steve Young
TL;DR
Early reinforcement-learning dialogue policies can perform poorly, posing a concern for online learning with real users. The paper combines sample-efficient actor-critic methods and demonstration-data pretraining; it reports competitive policies without significant detrimental impact on users.
Problem
Even sample-efficient reinforcement learning can have relatively poor initial performance, while supervised learning misses future action effects and states absent from its data.
Source: §1 · pp. 1–2Method
The paper introduces TRACER and eNACER for sample-efficient actor-critic learning and uses demonstration data to pre-train RL models before online learning.
Source: §1 · p. 2Results
The combined approach learns deep RL-based dialogue policies for new domains with competitive performance and no significant detrimental impact on users.
Source: §1 · p. 2Takeaways & Limitations
The paper presents a practical approach to learning deep RL-based dialogue policies by combining sample-efficient learning with demonstration-data pretraining.
Source: §1 · p. 2
Abstract
from arXiv · showhide
Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.
1 Introduction
Dialogue policy learning must balance reinforcement learning’s weak early performance with the limits of supervised dialogue data. The paper combines sample-efficient actor-critic methods with demonstration-data pretraining and reports competitive dialogue policies without significant detrimental impact on users.
1 Introduction
- Early RL performance remains relatively poor even with sample-efficient methods, potentially affecting the user experience during online learning.Supervised learning also misses future action effects and dialogue states absent from its training data, with problems exacerbated in larger domains requiring multi-step planning.Source: §1 · pp. 1–2
- The paper combines sample-efficient actor-critic methods with demonstration-based pretraining to learn dialogue policies for new domains.It frames the work as a network-based approach combining supervised and reinforcement-learning dialogue management.Source: §1 · p. 2
- TRACER uses trust regions to control actor-critic step size, while eNACER uses natural gradients for steepest policy-space ascent; both use off-policy experience replay.The paper introduces both algorithms to improve sample-efficiency and convergence.Source: §1 · p. 2
- Demonstration data pre-trains an RL model to mitigate cold start, with one model trained under both supervised and RL objectives without changing its architecture.The paper describes this as efficient use of demonstration data for improved early-stage policy learning.Source: §1 · p. 2
- The combined approach learns deep RL-based dialogue policies for new domains with competitive performance and no significant detrimental impact on users.The paper presents this as a practical approach to dialogue-policy learning.Source: §1 · p. 2
2 Related Work
Prior work addresses dialogue-policy learning through sample-efficient methods, supervised–reinforcement learning combinations, and neural policies. This paper distinguishes its approach by introducing actor-critic methods, combining two-stage learning with off-policy RL, and testing at differing noise levels.
2 Related Work
- Gaussian-process dialogue RL supports sample-efficient exploration and robustness to recognition or understanding errors, but its computation scales with stored points and sparse approximations limit scaling.The paper includes GP as a benchmark while questioning whether it scales to commercial wide-domain systems.Source: §2 · p. 2
- Reward shaping has been investigated as a way to enrich dialogue rewards and speed policy learning.The passage cites El Asri et al. (2014) and Su et al. (2015b).Source: §2 · p. 2
- Prior supervised–RL combinations restricted exploration to corpus states, initialized models with logistic regression, or used supervised corpora to pretrain Gaussian kernels without improving policy initialization.Other studied initialization approaches specified a GP prior or reused and pretrained an existing model for a new domain.Source: §2 · pp. 2–3
- Earlier neural-policy studies used two-stage training, while the work here combines two new sample-efficient actor-critic methods, two-stage learning, off-policy RL, and tests at differing noise levels.The cited earlier studies were conducted in simulation with error-free text-based input.Source: §2 · p. 3
3 Neural Dialogue Management
The framework selects semantic system actions from dialogue belief states, with remaining reply information completed from the tracked state. Its training discussion motivates policy-based RL and introduces A2C as the basis for TRACER improvements.
3 Neural Dialogue Management
- The dialogue-management model selects a semantic system action from a belief state, and heuristics use the tracked state to fill remaining reply information.The belief state encodes a distribution over user intents and dialogue history; the semantic reply is passed to a natural-language generator.Source: §3 · p. 3
3.1 Training with Reinforcement Learning
- Policy-based methods are favored for their convergence properties despite low sample-efficiency, high variance, and local-optimum risks; the paper also includes a value-based baseline.The objective is to maximize discounted dialogue return over turns, with each turn’s reward discounted by γ.Source: §3.1 · p. 3
3.1.1 Advantage Actor-Critic (A2C)
- A2C maximizes expected reward with a policy-gradient actor and a value-function baseline used to form the advantage; TD errors can approximate that advantage.The value function baseline reduces gradient variance without changing its estimated gradient.Source: §3.1.1 · pp. 3–4
3.1.2 The TRACER Algorithm
- The TRACER subsection introduces two methods to boost A2C policy learning.The supplied passage does not specify those methods in this subsection excerpt.Source: §3.1.2 · p. 4
I. Experience replay with off-policy learning for speed-up
The section combines off-policy experience replay with trust-region or natural-gradient updates to improve actor-critic learning. It also describes using demonstration data to initialize or guide reinforcement learning.
I. Experience replay with off-policy learning for speed-up
- TRACER combines experience replay, which reuses past samples for off-policy updates, with a KL trust region around an average policy to stabilize A2C.The trust region constrains policy deviation from the running average of past policies.Source: §I · pp. 4–5
- When the KL constraint is met, TRACER leaves the gradient unchanged; otherwise, it scales the update along the constraint direction to lower the policy change rate.This trust-region approach addresses the tension between stable, small updates and fast learning.Source: §I · pp. 4–5
3.1.3 The eNACER Algorithm
- eNACER uses the Fisher-corrected natural gradient for steepest ascent in policy space and replaces episodic returns with off-policy returns using experience replay.The Fisher correction makes the natural gradient independent of policy parametrization; the episodic version estimates gradients without explicitly computing the Fisher matrix.Source: §3.1.3 · p. 5
3.2 Learning from Demonstration Data
- Demonstration corpora can pre-train a policy to mimic responses, providing a starting point for RL despite fixed-data policies' limited generalization and lack of long-term planning.Demonstrations may come from Wizard-of-Oz collection or interactions with an existing policy.Source: §3.2 · p. 5
- A separate, persistent supervised replay pool adds sampled demonstration-data cross-entropy to the RL objective, guiding policy learning while it optimizes return.The supervised pool is not overwritten; the composite loss can also help alleviate early performance drops when pre-trained parameters differ from the optimal RL policy.Source: §3.2 · pp. 5–6
4 Experimental Results
The experiments evaluate dialogue-policy learning in a Cambridge restaurant system, comparing RL methods from scratch and testing demonstration-data strategies. Results favor combining supervised pre-training and replay for A2C ER, while TRACER provides a strong balance across initial performance, learning speed, and trained performance.
4 Experimental Results
- The target application was a live telephone-based restaurant-information system for Cambridge, with about 100 venues and six slots per venue.Three slots constrain searches and three provide information once a database entity is found.Source: §4 · p. 6
4.1 Model Comparison
- The comparison includes value-based DQN and GPRL alongside policy-based models; GPRL models its Q-function with a Gaussian process and uses a kernel to estimate values in unexplored space.The reported GPRL benchmark includes an uncertainty estimate to guide exploration.Source: §4.1 · p. 6, §4.1.2 · p. 6
4.1.1 Deep Q-Network (DQN)
- DQN approximates the Q-function with a neural network and minimizes a loss against a target computed using a less-frequently updated target network.The target uses replay-sampled transitions; Double DQN separates action selection from Q-value estimation to address over-estimation.Source: §4.1.1 · p. 6
4.1.2 Gaussian Processes (GP) RL
- GPRL learns quickly and relatively stably, while eNACER provides comparable performance in the noise-free scratch-learning comparison.TRACER was competitive but still lagged behind eNACER and GPRL.Source: §4.1.2 · p. 6, §4.2 · p. 7
4.2 Reinforcement Learning from Scratch
- In noise-free simulation, GP learned quickly and stably, eNACER performed comparably, and TRACER remained behind eNACER and GPRL.DQN showed high sample-efficiency but instability; A2C with ER learned more efficiently than on-policy A2C.Source: §4.2 · p. 7, Figure 2 · p. 7
4.2.1 Learning from Demonstration Data
- Combining supervised pre-training and replay achieved the best A2C ER result; overall, TRACER best balanced avoiding poor initial performance, rapid learning, and competitive fully trained performance.Figure 3 covers demonstration-data combinations; the TRACER conclusion also draws on the reported broader results.Source: §4.2.1 · p. 8, Figure 3 · p. 8
- Under varying semantic error rates, supervised learning produced a robust model that RL could further fine-tune, though performance dropped more rapidly at high noise than GPRL.The authors attribute the high-noise vulnerability to possible over-fitting in deep architectures and uncertainty in user behavior.Source: §4.2.1 · p. 8, Figure 4 · p. 8
5 Conclusion
The paper presents sample-efficient deep RL methods and demonstration-data approaches to address slow learning and poor early performance. It reports that TRACER performed best overall, while noting weaker performance in noise and the potential scalability of deep RL for larger user populations.
5 Conclusion
- TRACER and eNACER were more sample-efficient than other deep RL models and broadly competitive with GPRL; demonstration data mitigated poor early performance, with the approaches particularly effective alongside TRACER, which had the best overall performance.The demonstration-data approaches were simple supervised pre-training and placing corpus data in a replay buffer.Source: §5 · p. 9
- In mismatched environments, TRACER avoided poor initial performance after demonstration-corpus training and improved substantially with subsequent reinforcement learning, but its performance fell off more rapidly in noise than GPRL.The paper attributes the noise comparison to neural-network architectures not handling uncertainty estimates well.Source: §5 · p. 9
- The paper focuses on early learning in a new domain, where GPRL is a difficult-to-beat benchmark, and points to deep RL's potential to scale to large user populations.The paper states that model size is not related to the experience-replay buffer.Source: §5 · p. 9