TEACH: Temporal Variance-Driven Curriculum for Reinforcement Learning

• URL: http://arxiv.org/abs/2512.22824v1
• Date: Sun, 28 Dec 2025 07:29:29 GMT
• Title: TEACH: Temporal Variance-Driven Curriculum for Reinforcement Learning
• Authors: Gaurav Chaudhary, Laxmidhar Behera,
• Abstract summary: We propose a novel Student-Teacher learning paradigm with a Temporal Variance-Driven Curriculum to accelerate Goal-Conditioned RL.<n>In this framework, the teacher module dynamically prioritizes goals with the highest temporal variance in the policy's confidence score.<n>We demonstrate this through evaluation across 11 diverse robotic manipulation and maze navigation tasks.
• Score: 8.366600075241847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) has achieved significant success in solving single-goal tasks. However, uniform goal selection often results in sample inefficiency in multi-goal settings where agents must learn a universal goal-conditioned policy. Inspired by the adaptive and structured learning processes observed in biological systems, we propose a novel Student-Teacher learning paradigm with a Temporal Variance-Driven Curriculum to accelerate Goal-Conditioned RL. In this framework, the teacher module dynamically prioritizes goals with the highest temporal variance in the policy's confidence score, parameterized by the state-action value (Q) function. The teacher provides an adaptive and focused learning signal by targeting these high-uncertainty goals, fostering continual and efficient progress. We establish a theoretical connection between the temporal variance of Q-values and the evolution of the policy, providing insights into the method's underlying principles. Our approach is algorithm-agnostic and integrates seamlessly with existing RL frameworks. We demonstrate this through evaluation across 11 diverse robotic manipulation and maze navigation tasks. The results show consistent and notable improvements over state-of-the-art curriculum learning and goal-selection methods.

Related papers

• Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning [2.5352713493505785]
  Reinforcement learning -- algorithms that teach artificial agents to interact with environments by maximising reward signals -- has achieved significant success in recent years.<n>One promising research direction involves introducing goals to allow multimodal policies, commonly through hierarchical or curriculum reinforcement learning.<n>We present a novel probabilistic curriculum learning algorithm to suggest goals for reinforcement learning agents in continuous control and navigation tasks.
  arXiv  Detail & Related papers  (2025-04-02T08:15:16Z)
• Imitating Graph-Based Planning with Goal-Conditioned Policies [72.61631088613048]
  We present a self-imitation scheme which distills a subgoal-conditioned policy into the target-goal-conditioned policy. We empirically show that our method can significantly boost the sample-efficiency of the existing goal-conditioned RL methods.
  arXiv  Detail & Related papers  (2023-03-20T14:51:10Z)
• Goal-Conditioned Q-Learning as Knowledge Distillation [136.79415677706612]
  We explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. We empirically show that this can improve the performance of goal-conditioned off-policy reinforcement learning when the space of goals is high-dimensional. We also show that this technique can be adapted to allow for efficient learning in the case of multiple simultaneous sparse goals.
  arXiv  Detail & Related papers  (2022-08-28T22:01:10Z)
• Automatic Goal Generation using Dynamical Distance Learning [5.797847756967884]
  Reinforcement Learning (RL) agents can learn to solve complex sequential decision making tasks by interacting with the environment. In the field of multi-goal RL, where agents are required to reach multiple goals to solve complex tasks, improving sample efficiency can be especially challenging. We propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.
  arXiv  Detail & Related papers  (2021-11-07T16:23:56Z)
• C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks [133.40619754674066]
  Goal-conditioned reinforcement learning can solve tasks in a wide range of domains, including navigation and manipulation. We propose the distant goal-reaching task by using search at training time to automatically generate intermediate states. E-step corresponds to planning an optimal sequence of waypoints using graph search, while the M-step aims to learn a goal-conditioned policy to reach those waypoints.
  arXiv  Detail & Related papers  (2021-10-22T22:05:31Z)
• Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning [114.07623388322048]
  We discuss how the standard goal-conditioned RL (GCRL) is encapsulated by the objective variational empowerment. Our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL.
  arXiv  Detail & Related papers  (2021-06-02T18:12:26Z)
• Adversarial Intrinsic Motivation for Reinforcement Learning [60.322878138199364]
  We investigate whether the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution can be utilized effectively for reinforcement learning tasks. Our approach, termed Adversarial Intrinsic Motivation (AIM), estimates this Wasserstein-1 distance through its dual objective and uses it to compute a supplemental reward function.
  arXiv  Detail & Related papers  (2021-05-27T17:51:34Z)
• Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
  Continually solving new, unsolved tasks is the key to learning diverse behaviors. In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. We propose setting up an automatic curriculum for goals that the agent needs to solve. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-17T03:58:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.