A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2108.12916v1
• Date: Sun, 29 Aug 2021 20:51:32 GMT
• Title: A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
• Authors: Tianchi Cai, Wenpeng Zhang, Lihong Gu, Xiaodong Zeng, Jinjie Gu
• Abstract summary: We propose a new variant of the conditional gradient (CG) type algorithm, which generalizes the minimum norm point (MNP) method. Our method reduces the memory costs by an order of magnitude, and achieves better performance, demonstrating both its effectiveness and efficiency.
• Score: 2.811714058940267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that can randomize among multiple actions. To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy. However, these approaches require storing a large set of policies, which is not policy efficient, and may incur prohibitive memory costs in constrained deep RL. To address this problem, we propose an alternative approach. Our approach first reformulates the CRL to an equivalent distance optimization problem. With a specially designed linear optimization oracle, we derive a meta-algorithm that solves it using any off-the-shelf RL algorithm and any conditional gradient (CG) type algorithm as subroutines. We then propose a new variant of the CG-type algorithm, which generalizes the minimum norm point (MNP) method. The proposed method matches the convergence rate of the existing game-theoretic approaches and achieves the worst-case optimal policy efficiency. The experiments on a navigation task show that our method reduces the memory costs by an order of magnitude, and meanwhile achieves better performance, demonstrating both its effectiveness and efficiency.

Related papers

• Iteratively Refined Behavior Regularization for Offline Reinforcement Learning [57.10922880400715]
  In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. By iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks.
  arXiv  Detail & Related papers  (2023-06-09T07:46:24Z)
• Offline Policy Optimization in RL with Variance Regularizaton [142.87345258222942]
  We propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms.
  arXiv  Detail & Related papers  (2022-12-29T18:25:01Z)
• Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics [5.270497591225775]
  In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. We propose a novel algorithm for constrained RL that does not suffer from these limitations.
  arXiv  Detail & Related papers  (2022-12-03T01:54:55Z)
• OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation [59.469401906712555]
  We present an offline reinforcement learning algorithm that prevents overestimation in a more principled way. Our algorithm, OptiDICE, directly estimates the stationary distribution corrections of the optimal policy. We show that OptiDICE performs competitively with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-21T00:43:30Z)
• Learning Sampling Policy for Faster Derivative Free Optimization [100.27518340593284]
  We propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. Our results show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
  arXiv  Detail & Related papers  (2021-04-09T14:50:59Z)
• Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization [5.072893872296332]
  Action-constrained reinforcement learning (RL) is a widely-used approach in various real-world applications. We propose a learning algorithm that decouples the action constraints from the policy parameter update. We show that the proposed algorithm significantly outperforms the benchmark methods on a variety of control tasks.
  arXiv  Detail & Related papers  (2021-02-22T14:28:03Z)
• CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee [61.176159046544946]
  In safe reinforcement learning (SRL) problems, an agent explores the environment to maximize an expected total reward and avoids violation of certain constraints. This is the first-time analysis of SRL algorithms with global optimal policies.
  arXiv  Detail & Related papers  (2020-11-11T16:05:14Z)
• Variance-Reduced Off-Policy Memory-Efficient Policy Search [61.23789485979057]
  Off-policy policy optimization is a challenging problem in reinforcement learning. Off-policy algorithms are memory-efficient and capable of learning from off-policy samples.
  arXiv  Detail & Related papers  (2020-09-14T16:22:46Z)
• Queueing Network Controls via Deep Reinforcement Learning [0.0]
  We develop a Proximal policy optimization algorithm for queueing networks. The algorithm consistently generates control policies that outperform state-of-arts in literature. A key to the successes of our PPO algorithm is the use of three variance reduction techniques in estimating the relative value function.
  arXiv  Detail & Related papers  (2020-07-31T01:02:57Z)

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.