Reinforcement Learning with Elastic Time Steps

• URL: http://arxiv.org/abs/2402.14961v3
• Date: Wed, 3 Jul 2024 00:31:18 GMT
• Title: Reinforcement Learning with Elastic Time Steps
• Authors: Dong Wang, Giovanni Beltrame,
• Abstract summary: Multi-Objective Soft Elastic Actor-Critic (MOSEAC) is an off-policy actor-critic algorithm that uses elastic time steps to dynamically adjust the control frequency. We show that MOSEAC converges and produces stable policies at the theoretical level, and validate our findings in a real-time 3D racing game.
• Score: 14.838483990647697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional Reinforcement Learning (RL) policies are typically implemented with fixed control rates, often disregarding the impact of control rate selection. This can lead to inefficiencies as the optimal control rate varies with task requirements. We propose the Multi-Objective Soft Elastic Actor-Critic (MOSEAC), an off-policy actor-critic algorithm that uses elastic time steps to dynamically adjust the control frequency. This approach minimizes computational resources by selecting the lowest viable frequency. We show that MOSEAC converges and produces stable policies at the theoretical level, and validate our findings in a real-time 3D racing game. MOSEAC significantly outperformed other variable time step approaches in terms of energy efficiency and task effectiveness. Additionally, MOSEAC demonstrated faster and more stable training, showcasing its potential for real-world RL applications in robotics.

Related papers

• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• MOSEAC: Streamlined Variable Time Step Reinforcement Learning [14.838483990647697]
  We introduce the Multi-Objective Soft Elastic Actor-Critic (MOSEAC) method. MOSEAC features an adaptive reward scheme based on observed trends in task rewards during training. We validate the MOSEAC method through simulations in a Newtonian kinematics environment.
  arXiv  Detail & Related papers  (2024-06-03T16:51:57Z)
• When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL [37.58940726230092]
  Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP) We formalize an RL framework, Time-adaptive Control & Sensing (TaCoS), that tackles this challenge. We demonstrate that state-of-the-art RL algorithms trained on TaCoS drastically reduce the interaction amount over their discrete-time counterpart.
  arXiv  Detail & Related papers  (2024-06-03T09:57:18Z)
• Deployable Reinforcement Learning with Variable Control Rate [14.838483990647697]
  We propose a variant of Reinforcement Learning (RL) with variable control rate. In this approach, the policy decides the action the agent should take as well as the duration of the time step associated with that action. We show the efficacy of SEAC through a proof-of-concept simulation driving an agent with Newtonian kinematics.
  arXiv  Detail & Related papers  (2024-01-17T15:40:11Z)
• Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning [68.16998247593209]
  offline reinforcement learning (RL) paradigm provides recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. In this paper, we propose an adaptive scheme for action quantization. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme.
  arXiv  Detail & Related papers  (2023-10-18T06:07:10Z)
• CCE: Sample Efficient Sparse Reward Policy Learning for Robotic Navigation via Confidence-Controlled Exploration [72.24964965882783]
  Confidence-Controlled Exploration (CCE) is designed to enhance the training sample efficiency of reinforcement learning algorithms for sparse reward settings such as robot navigation. CCE is based on a novel relationship we provide between gradient estimation and policy entropy. We demonstrate through simulated and real-world experiments that CCE outperforms conventional methods that employ constant trajectory lengths and entropy regularization.
  arXiv  Detail & Related papers  (2023-06-09T18:45:15Z)
• Dynamic Decision Frequency with Continuous Options [11.83290684845269]
  In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. We propose a framework called Continuous-Time Continuous-Options (CTCO) where the agent chooses options as sub-policies of variable durations. We show that our algorithm's performance is not affected by the choice of environment interaction frequency.
  arXiv  Detail & Related papers  (2022-12-06T19:51:12Z)
• Accelerated Policy Learning with Parallel Differentiable Simulation [59.665651562534755]
  We present a differentiable simulator and a new policy learning algorithm (SHAC) Our algorithm alleviates problems with local minima through a smooth critic function. We show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms.
  arXiv  Detail & Related papers  (2022-04-14T17:46:26Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

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.