Related papers: A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints

A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints

URL: http://arxiv.org/abs/2404.16468v1
Date: Thu, 25 Apr 2024 09:50:57 GMT
Title: A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints
Authors: Bram De Cooman, Johan Suykens,
Abstract summary: We use a generic primal-dual framework for value-based and actor-critic reinforcement learning methods. The obtained dual formulations turn out to be especially useful for imposing additional constraints on the learned policy. A practical algorithm is derived that supports various combinations of policy constraints that are automatically handled throughout training.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Model-free reinforcement learning methods lack an inherent mechanism to impose behavioural constraints on the trained policies. While certain extensions exist, they remain limited to specific types of constraints, such as value constraints with additional reward signals or visitation density constraints. In this work we try to unify these existing techniques and bridge the gap with classical optimization and control theory, using a generic primal-dual framework for value-based and actor-critic reinforcement learning methods. The obtained dual formulations turn out to be especially useful for imposing additional constraints on the learned policy, as an intrinsic relationship between such dual constraints (or regularization terms) and reward modifications in the primal is reveiled. Furthermore, using this framework, we are able to introduce some novel types of constraints, allowing to impose bounds on the policy's action density or on costs associated with transitions between consecutive states and actions. From the adjusted primal-dual optimization problems, a practical algorithm is derived that supports various combinations of policy constraints that are automatically handled throughout training using trainable reward modifications. The resulting $\texttt{DualCRL}$ method is examined in more detail and evaluated under different (combinations of) constraints on two interpretable environments. The results highlight the efficacy of the method, which ultimately provides the designer of such systems with a versatile toolbox of possible policy constraints.

Related papers

Adversarial Constrained Policy Optimization: Improving Constrained Reinforcement Learning by Adapting Budgets [6.5472155063246085]
Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. We propose Adrial Constrained Policy Optimization (ACPO), which enables simultaneous optimization of reward and the adaptation of cost budgets during training.
arXiv Detail & Related papers (2024-10-28T07:04:32Z)
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning [62.81324245896717]
We introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-ite convergence guarantees under (weak) gradient domination assumptions. We numerically validate our algorithms on constrained control problems, and compare them with state-of-the-art baselines.
arXiv Detail & Related papers (2024-07-15T14:54:57Z)
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning [26.244121960815907]
We propose a primal-based framework that orchestrates policy optimization between multi-objective learning and constraint adherence. Our method employs a novel natural policy gradient manipulation method to optimize multiple RL objectives. Empirically, our proposed method also outperforms prior state-of-the-art methods on challenging safe multi-objective reinforcement learning tasks.
arXiv Detail & Related papers (2024-05-26T00:42:10Z)
Resilient Constrained Reinforcement Learning [87.4374430686956]
We study a class of constrained reinforcement learning (RL) problems in which multiple constraint specifications are not identified before study. It is challenging to identify appropriate constraint specifications due to the undefined trade-off between the reward training objective and the constraint satisfaction. We propose a new constrained RL approach that searches for policy and constraint specifications together.
arXiv Detail & Related papers (2023-12-28T18:28:23Z)
On Regularization and Inference with Label Constraints [62.60903248392479]
We compare two strategies for encoding label constraints in a machine learning pipeline, regularization with constraints and constrained inference. For regularization, we show that it narrows the generalization gap by precluding models that are inconsistent with the constraints. For constrained inference, we show that it reduces the population risk by correcting a model's violation, and hence turns the violation into an advantage.
arXiv Detail & Related papers (2023-07-08T03:39:22Z)
Pragmatic Fairness: Developing Policies with Outcome Disparity Control [15.618754942472822]
We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We propose two different fairness constraints: a moderation breaking constraint and an equal benefit constraint.
arXiv Detail & Related papers (2023-01-28T19:25:56Z)
Penalized Proximal Policy Optimization for Safe Reinforcement Learning [68.86485583981866]
We propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. P3O utilizes a simple-yet-effective penalty function to eliminate cost constraints and removes the trust-region constraint by the clipped surrogate objective. We show that P3O outperforms state-of-the-art algorithms with respect to both reward improvement and constraint satisfaction on a set of constrained locomotive tasks.
arXiv Detail & Related papers (2022-05-24T06:15:51Z)
A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
arXiv Detail & Related papers (2022-02-19T20:22:04Z)
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards [88.30521204048551]
A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. We show a simple example in which the desired optimal policy cannot be induced by any weighted linear combination of rewards. This work addresses this shortcoming by augmenting the state with Lagrange multipliers and reinterpreting primal-dual methods.
arXiv Detail & Related papers (2021-02-23T21:07:35Z)
Deep Constrained Q-learning [15.582910645906145]
In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a set of constraints. We propose Constrained Q-learning, a novel off-policy reinforcement learning framework restricting the action space directly in the Q-update to learn the optimal Q-function for the induced constrained MDP and the corresponding safe policy.
arXiv Detail & Related papers (2020-03-20T17:26:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.