Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving

• URL: http://arxiv.org/abs/2103.13861v1
• Date: Thu, 25 Mar 2021 14:19:54 GMT
• Title: Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving
• Authors: Briti Gangopadhyay, Harshit Soora, Pallab Dasgupta
• Abstract summary: Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have demonstrated impressive performance in complex tasks, including autonomous driving. We propose HPRL - Hierarchical Program-triggered Reinforcement Learning, which uses a hierarchy consisting of a structured program along with multiple RL agents, each trained to perform a relatively simple task. The focus of verification shifts to the master program under simple guarantees from the RL agents, leading to a significantly more interpretable and verifiable implementation as compared to a complex RL agent.
• Score: 5.404179497338455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have demonstrated impressive performance in complex tasks, including autonomous driving. The use of RL agents in autonomous driving leads to a smooth human-like driving experience, but the limited interpretability of Deep Reinforcement Learning (DRL) creates a verification and certification bottleneck. Instead of relying on RL agents to learn complex tasks, we propose HPRL - Hierarchical Program-triggered Reinforcement Learning, which uses a hierarchy consisting of a structured program along with multiple RL agents, each trained to perform a relatively simple task. The focus of verification shifts to the master program under simple guarantees from the RL agents, leading to a significantly more interpretable and verifiable implementation as compared to a complex RL agent. The evaluation of the framework is demonstrated on different driving tasks, and NHTSA precrash scenarios using CARLA, an open-source dynamic urban simulation environment.

Related papers

• Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards [49.7719149179179]
  This paper investigates the feasibility of using PPO for reinforcement learning (RL) from explicitly programmed reward signals. We focus on tasks expressed through formal languages, such as programming, where explicit reward functions can be programmed to automatically assess quality of generated outputs. Our results show that pure RL-based training for the two formal language tasks is challenging, with success being limited even for the simple arithmetic task.
  arXiv  Detail & Related papers  (2024-10-22T15:59:58Z)
• ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL [80.10358123795946]
  We develop a framework for building multi-turn RL algorithms for fine-tuning large language models. Our framework adopts a hierarchical RL approach and runs two RL algorithms in parallel. Empirically, we find that ArCHer significantly improves efficiency and performance on agent tasks.
  arXiv  Detail & Related papers  (2024-02-29T18:45:56Z)
• RL-GPT: Integrating Reinforcement Learning and Code-as-policy [82.1804241891039]
  We introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent. The slow agent analyzes actions suitable for coding, while the fast agent executes coding tasks. This decomposition effectively focuses each agent on specific tasks, proving highly efficient within our pipeline.
  arXiv  Detail & Related papers  (2024-02-29T16:07:22Z)
• Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents [9.529492371336286]
  Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. We propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS) LSTS learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification.
  arXiv  Detail & Related papers  (2024-02-06T04:00:21Z)
• Automated Reinforcement Learning (AutoRL): A Survey and Open Problems [92.73407630874841]
  Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL. We provide a common taxonomy, discuss each area in detail and pose open problems which would be of interest to researchers going forward.
  arXiv  Detail & Related papers  (2022-01-11T12:41:43Z)
• DriverGym: Democratising Reinforcement Learning for Autonomous Driving [75.91049219123899]
  We propose DriverGym, an open-source environment for developing reinforcement learning algorithms for autonomous driving. DriverGym provides access to more than 1000 hours of expert logged data and also supports reactive and data-driven agent behavior. The performance of an RL policy can be easily validated on real-world data using our extensive and flexible closed-loop evaluation protocol.
  arXiv  Detail & Related papers  (2021-11-12T11:47:08Z)
• Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions [0.0]
  This paper focuses on investigating the value of curriculum reinforcement learning in autonomous driving applications. We setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity.
  arXiv  Detail & Related papers  (2021-03-14T12:05:05Z)
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control [65.00425082663146]
  We present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control. In KTM-DRL, the multi-task agent first leverages an offline knowledge transfer algorithm to quickly learn a control policy from the experience of task-specific teachers. The experimental results well justify the effectiveness of KTM-DRL and its knowledge transfer and online learning algorithms, as well as its superiority over the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2020-10-15T03:26:47Z)
• Self-Paced Deep Reinforcement Learning [42.467323141301826]
  Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. We propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms.
  arXiv  Detail & Related papers  (2020-04-24T15:48:07Z)
• Guiding Robot Exploration in Reinforcement Learning via Automated Planning [6.075903612065429]
  Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals. automated planning aims to compute plans for accomplishing tasks using action knowledge. We develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states.
  arXiv  Detail & Related papers  (2020-04-23T21:03:30Z)
• Deep Reinforcement Learning for Autonomous Driving: A Survey [0.3694429692322631]
  This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.
  arXiv  Detail & Related papers  (2020-02-02T18:21:22Z)

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.