RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

• URL: http://arxiv.org/abs/2405.03064v3
• Date: Thu, 6 Jun 2024 00:36:03 GMT
• Title: RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
• Authors: Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing,
• Abstract summary: RICE is an innovative refining scheme for reinforcement learning. It incorporates explanation methods to break through the training bottlenecks. We evaluate RICE in various popular RL environments and real-world applications.
• Score: 40.84214941048131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, especially with sparse rewards, remains a significant challenge. The training of a DRL agent can be often trapped in a bottleneck without further progress. In this paper, we propose RICE, an innovative refining scheme for reinforcement learning that incorporates explanation methods to break through the training bottlenecks. The high-level idea of RICE is to construct a new initial state distribution that combines both the default initial states and critical states identified through explanation methods, thereby encouraging the agent to explore from the mixed initial states. Through careful design, we can theoretically guarantee that our refining scheme has a tighter sub-optimality bound. We evaluate RICE in various popular RL environments and real-world applications. The results demonstrate that RICE significantly outperforms existing refining schemes in enhancing agent performance.

Related papers

• Random Latent Exploration for Deep Reinforcement Learning [71.88709402926415]
  This paper introduces a new exploration technique called Random Latent Exploration (RLE) RLE combines the strengths of bonus-based and noise-based (two popular approaches for effective exploration in deep RL) exploration strategies. We evaluate it on the challenging Atari and IsaacGym benchmarks and show that RLE exhibits higher overall scores across all the tasks than other approaches.
  arXiv  Detail & Related papers  (2024-07-18T17:55:22Z)
• RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning [50.55776190278426]
  Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. We introduce RLeXplore, a unified, highly modularized, and plug-and-play framework offering reliable implementations of eight state-of-the-art intrinsic reward algorithms.
  arXiv  Detail & Related papers  (2024-05-29T22:23:20Z)
• Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach [11.11570399751075]
  This research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL) The integration of our DRL agent with the RAY platform facilitates the creation of RLlib-IMPALA, a novel framework that efficiently uses RAY's resources to improve system adaptability and control.
  arXiv  Detail & Related papers  (2024-02-24T23:25:35Z)
• Imitation Bootstrapped Reinforcement Learning [31.916571349600684]
  imitation bootstrapped reinforcement learning (IBRL) is a novel framework for sample-efficient reinforcement learning. We evaluate IBRL on 6 simulation and 3 real-world tasks spanning various difficulty levels.
  arXiv  Detail & Related papers  (2023-11-03T19:03:20Z)
• When Learning Is Out of Reach, Reset: Generalization in Autonomous Visuomotor Reinforcement Learning [10.469509984098705]
  Episodic training, where an agent's environment is reset after every success or failure, is the de facto standard when training embodied reinforcement learning (RL) agents. In this work, we look to minimize, rather than completely eliminate, resets while building visual agents that can meaningfully generalize. Our proposed approach significantly outperforms prior episodic, reset-free, and reset-minimizing approaches achieving higher success rates.
  arXiv  Detail & Related papers  (2023-03-30T17:59:26Z)
• Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality [141.89413461337324]
  Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL) We propose a theoretical formulation for deployment-efficient RL (DE-RL) from an "optimization with constraints" perspective.
  arXiv  Detail & Related papers  (2022-02-14T01:31:46Z)
• 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)
• Nested-Wasserstein Self-Imitation Learning for Sequence Generation [158.19606942252284]
  We propose the concept of nested-Wasserstein distance for distributional semantic matching. A novel nested-Wasserstein self-imitation learning framework is developed, encouraging the model to exploit historical high-rewarded sequences.
  arXiv  Detail & Related papers  (2020-01-20T02:19:13Z)

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.