Causal-Paced Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2507.02910v1
• Date: Tue, 24 Jun 2025 20:15:01 GMT
• Title: Causal-Paced Deep Reinforcement Learning
• Authors: Geonwoo Cho, Jaegyun Im, Doyoon Kim, Sundong Kim,
• Abstract summary: Causal-Paced Deep Reinforcement Learning (CP-DRL) is a curriculum learning framework aware of SCM differences between tasks based on interaction data approximation.<n> Empirically, CP-DRL outperforms existing curriculum methods on the Point Mass benchmark.
• Score: 4.728991543521559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing effective task sequences is crucial for curriculum reinforcement learning (CRL), where agents must gradually acquire skills by training on intermediate tasks. A key challenge in CRL is to identify tasks that promote exploration, yet are similar enough to support effective transfer. While recent approach suggests comparing tasks via their Structural Causal Models (SCMs), the method requires access to ground-truth causal structures, an unrealistic assumption in most RL settings. In this work, we propose Causal-Paced Deep Reinforcement Learning (CP-DRL), a curriculum learning framework aware of SCM differences between tasks based on interaction data approximation. This signal captures task novelty, which we combine with the agent's learnability, measured by reward gain, to form a unified objective. Empirically, CP-DRL outperforms existing curriculum methods on the Point Mass benchmark, achieving faster convergence and higher returns. CP-DRL demonstrates reduced variance with comparable final returns in the Bipedal Walker-Trivial setting, and achieves the highest average performance in the Infeasible variant. These results indicate that leveraging causal relationships between tasks can improve the structure-awareness and sample efficiency of curriculum reinforcement learning. We provide the full implementation of CP-DRL to facilitate the reproduction of our main results at https://github.com/Cho-Geonwoo/CP-DRL.

Related papers

• Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training [121.5858973157225]
  We investigate the effects of prolonged reinforcement learning on a small language model across a diverse set of reasoning domains.<n>We introduce controlled KL regularization, clipping ratio, and periodic reference policy resets as critical components for unlocking long-term performance gains.<n>Our model achieves significant improvements over strong baselines, including +14.7% on math, +13.9% on coding, and +54.8% on logic puzzle tasks.
  arXiv  Detail & Related papers  (2025-07-16T17:59:24Z)
• KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning [72.53466291156604]
  We present textbfKDRL, a textitunified post-training framework that jointly optimize a reasoning model through teacher supervision (KD) and self-exploration (RL)<n>We first formulate a unified objective that integrates GRPO and KD, and systematically explore how different KL approximations, KL coefficients, and reward-guided KD strategies affect the overall post-training dynamics and performance.
  arXiv  Detail & Related papers  (2025-06-02T19:46:41Z)
• Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation [6.683222869973898]
  Reinforcement learning (RL) has demonstrated remarkable potential in robotic manipulation but faces challenges in sample inefficiency and lack of interpretability.<n>This paper proposes a Knowledge Capture, Adaptation, and Composition framework to integrate knowledge transfer into RL through cross-task curriculum learning.<n>As a result, our KCAC approach achieves a 40 percent reduction in training time while improving task success rates by 10 percent compared to traditional RL methods.
  arXiv  Detail & Related papers  (2025-05-15T17:30:29Z)
• CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representation [13.575628222213387]
  CausalCOMRL is a context-based OMRL method that integrates causal representation learning.<n>We show that CausalCOMRL achieves better performance than other methods on most benchmarks.
  arXiv  Detail & Related papers  (2025-02-03T01:43:54Z)
• VinePPO: Refining Credit Assignment in RL Training of LLMs [66.80143024475635]
  We propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates.<n>Our method consistently outperforms PPO and other baselines across MATH and GSM8K datasets in less wall-clock time.
  arXiv  Detail & Related papers  (2024-10-02T15:49:30Z)
• Proximal Curriculum with Task Correlations for Deep Reinforcement Learning [25.10619062353793]
  We consider curriculum design in contextual multi-task settings where the agent's final performance is measured w.r.t. a target distribution over complex tasks. We propose a novel curriculum, ProCuRL-Target, that effectively balances the need for selecting tasks that are not too difficult for the agent while progressing the agent's learning toward the target distribution via leveraging task correlations.
  arXiv  Detail & Related papers  (2024-05-03T21:07:54Z)
• On the Benefit of Optimal Transport for Curriculum Reinforcement Learning [32.59609255906321]
  We focus on framing curricula ass between task distributions. We frame the generation of a curriculum as a constrained optimal transport problem. Benchmarks show that this way of curriculum generation can improve upon existing CRL methods.
  arXiv  Detail & Related papers  (2023-09-25T12:31:37Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation [46.103426976842336]
  Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks. In this work, we focus on the idea of framing CRL as Curriculums between a source (auxiliary) and a target task distribution. Inspired by the insights from gradual domain adaptation in semi-supervised learning, we create a natural curriculum by breaking down the potentially large task distributional shift in CRL into smaller shifts.
  arXiv  Detail & Related papers  (2022-10-18T22:33:33Z)
• DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV [65.07776277630228]
  We propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework (DCF) Particularly, we design an encoder-decoder structured policy network in our upper-level DRL model to allocate the tasks to different UAVs. We also exploit another attention based policy network in our lower-level DRL model to construct the route for each UAV, with the objective to maximize the number of executed tasks.
  arXiv  Detail & Related papers  (2022-08-04T04:35:53Z)
• Meta Reinforcement Learning with Successor Feature Based Context [51.35452583759734]
  We propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms. Our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training.
  arXiv  Detail & Related papers  (2022-07-29T14:52:47Z)
• Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks [56.63855534940827]
  This work introduces a novel objective function to learn an action translator among training tasks. We theoretically verify that the value of the transferred policy with the action translator can be close to the value of the source policy. We propose to combine the action translator with context-based meta-RL algorithms for better data collection and more efficient exploration during meta-training.
  arXiv  Detail & Related papers  (2022-07-19T04:58:06Z)

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.