Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Off-policy deep reinforcement learning (RL) has
been successful in a range of challenging domains.
However, standard off-policy RL algorithms can
suffer from several issues, such as instability in Qlearning and balancing exploration and exploitation. To mitigate these issues, we present SUNRISE, a simple unified ensemble method, which
is compatible with various off-policy RL algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on
uncertainty estimates from a Q-ensemble, and
(b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration. By enforcing the diversity
between agents using Bootstrap with random initialization, we show that these different ideas
are largely orthogonal and can be fruitfully integrated, together further improving the performance of existing off-policy RL algorithms, such
as Soft Actor-Critic and Rainbow DQN, for both
continuous and discrete control tasks on both
low-dimensional and high-dimensional environments.
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- Award ID(s):
- 2024675
- NSF-PAR ID:
- 10300404
- Date Published:
- Journal Name:
- International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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