The Role of Diverse Replay for Generalisation in Reinforcement Learning

• URL: http://arxiv.org/abs/2306.05727v2
• Date: Thu, 31 Aug 2023 10:54:50 GMT
• Title: The Role of Diverse Replay for Generalisation in Reinforcement Learning
• Authors: Max Weltevrede, Matthijs T.J. Spaan, Wendelin B\"ohmer
• Abstract summary: We investigate the impact of the exploration strategy and replay buffer on generalisation in reinforcement learning. We show that collecting and training on more diverse data from the training environments will improve zero-shot generalisation to new tasks.
• Score: 7.399291598113285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In reinforcement learning (RL), key components of many algorithms are the exploration strategy and replay buffer. These strategies regulate what environment data is collected and trained on and have been extensively studied in the RL literature. In this paper, we investigate the impact of these components in the context of generalisation in multi-task RL. We investigate the hypothesis that collecting and training on more diverse data from the training environments will improve zero-shot generalisation to new tasks. We motivate mathematically and show empirically that generalisation to tasks that are "reachable'' during training is improved by increasing the diversity of transitions in the replay buffer. Furthermore, we show empirically that this same strategy also shows improvement for generalisation to similar but "unreachable'' tasks which could be due to improved generalisation of the learned latent representations.

Related papers

• Training on more Reachable Tasks for Generalisation in Reinforcement Learning [5.855552389030083]
  In multi-task reinforcement learning, agents train on a fixed set of tasks and have to generalise to new ones. Recent work has shown that increased exploration improves this generalisation, but it remains unclear why exactly that is. We introduce the concept of reachability in multi-task reinforcement learning and show that an initial exploration phase increases the number of reachable tasks the agent is trained on.
  arXiv  Detail & Related papers  (2024-10-04T16:15:31Z)
• Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders [63.28408887247742]
  We study whether training procedures can be improved to yield better generalization capabilities in the resulting models. We recommend a simple recipe for training dense encoders: Train on MSMARCO with parameter-efficient methods, such as LoRA, and opt for using in-batch negatives unless given well-constructed hard negatives.
  arXiv  Detail & Related papers  (2023-11-16T10:42:58Z)
• On the Importance of Exploration for Generalization in Reinforcement Learning [89.63074327328765]
  We propose EDE: Exploration via Distributional Ensemble, a method that encourages exploration of states with high uncertainty. Our algorithm is the first value-based approach to achieve state-of-the-art on both Procgen and Crafter.
  arXiv  Detail & Related papers  (2023-06-08T18:07:02Z)
• A Game-Theoretic Perspective of Generalization in Reinforcement Learning [9.402272029807316]
  Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. We propose a game-theoretic framework for the generalization in reinforcement learning, named GiRL.
  arXiv  Detail & Related papers  (2022-08-07T06:17:15Z)
• Contextualize Me -- The Case for Context in Reinforcement Learning [49.794253971446416]
  Contextual Reinforcement Learning (cRL) provides a framework to model such changes in a principled manner. We show how cRL contributes to improving zero-shot generalization in RL through meaningful benchmarks and structured reasoning about generalization tasks.
  arXiv  Detail & Related papers  (2022-02-09T15:01:59Z)
• Return-Based Contrastive Representation Learning for Reinforcement Learning [126.7440353288838]
  We propose a novel auxiliary task that forces the learnt representations to discriminate state-action pairs with different returns. Our algorithm outperforms strong baselines on complex tasks in Atari games and DeepMind Control suite.
  arXiv  Detail & Related papers  (2021-02-22T13:04:18Z)
• Instance based Generalization in Reinforcement Learning [24.485597364200824]
  We analyze policy learning in the context of Partially Observable Markov Decision Processes (POMDPs) We prove that, independently of the exploration strategy, reusing instances introduces significant changes on the effective Markov dynamics the agent observes during training. We propose training a shared belief representation over an ensemble of specialized policies, from which we compute a consensus policy that is used for data collection, disallowing instance specific exploitation.
  arXiv  Detail & Related papers  (2020-11-02T16:19:44Z)
• Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning [90.93035276307239]
  We propose an information theoretic regularization objective and an annealing-based optimization method to achieve better generalization ability in RL agents. We demonstrate the extreme generalization benefits of our approach in different domains ranging from maze navigation to robotic tasks. This work provides a principled way to improve generalization in RL by gradually removing information that is redundant for task-solving.
  arXiv  Detail & Related papers  (2020-08-03T02:24:20Z)
• Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning [67.34810824996887]
  Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. We propose Iterated Relearning (ITER) to improve generalisation of deep RL agents.
  arXiv  Detail & Related papers  (2020-06-10T13:26:31Z)

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.