Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

• URL: http://arxiv.org/abs/2104.08876v1
• Date: Sun, 18 Apr 2021 15:04:37 GMT
• Title: Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
• Authors: Songan Zhang, Lu Wen, Huei Peng, H. Eric Tseng
• Abstract summary: We develop Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. Two types of distribution variation in environments were designed and simulated to validate the fast adaptation capability of resulting MRL driving policies.
• Score: 15.570621284198017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions. While deep reinforcement learning methods have excelled in recent years and been applied to automated vehicle driving policy, there are concerns about their capability to quickly adapt to unseen traffic with new environment dynamics. We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. Two types of distribution variation in environments were designed and simulated to validate the fast adaptation capability of resulting MRL driving policies which significantly outperform a baseline RL.

Related papers

• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning [7.2282857478457805]
  "DRNet" is a novel DRL-based framework that enables a DRL agent to learn to drive by executing reasonable lane changing on simulated highways. Our DRL agent has the ability to learn the desired task without causing collisions and outperforms DDQN and other baseline models.
  arXiv  Detail & Related papers  (2023-11-02T21:17:52Z)
• Robust Driving Policy Learning with Guided Meta Reinforcement Learning [49.860391298275616]
  We introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy. By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy. We propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy.
  arXiv  Detail & Related papers  (2023-07-19T17:42:36Z)
• Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach [6.961253535504979]
  This paper introduces a novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logics (DRLSL) It combines the strengths of DRL (learning from experience) and symbolic first-order logics (knowledge-driven reasoning) to enable safe learning in real-time interactions of autonomous driving within real environments. We have implemented the DRLSL framework in autonomous driving using the highD dataset and demonstrated that our method successfully avoids unsafe actions during both the training and testing phases.
  arXiv  Detail & Related papers  (2023-07-03T19:43:21Z)
• Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers [0.4241054493737716]
  This study implements, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO) Models trained on the designed ComplexRoads environment can adapt well to other driving maneuvers with promising overall performance.
  arXiv  Detail & Related papers  (2023-06-20T11:41:01Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
  Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
  arXiv  Detail & Related papers  (2021-01-04T03:06:08Z)
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon [14.059728921828938]
  This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. The PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability.
  arXiv  Detail & Related papers  (2020-08-26T22:49:27Z)
• Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings [129.80279257258098]
  Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous. We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
  arXiv  Detail & Related papers  (2020-08-15T01:40:59Z)
• Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning [10.909595997847443]
  Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning. The trained agent is able to learn a smooth, safe, and efficient driving policy to make lane-change decisions.
  arXiv  Detail & Related papers  (2020-02-07T08:43:34Z)

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.