Learning Value Functions in Deep Policy Gradients using Residual Variance

• URL: http://arxiv.org/abs/2010.04440v3
• Date: Mon, 15 Mar 2021 18:51:46 GMT
• Title: Learning Value Functions in Deep Policy Gradients using Residual Variance
• Authors: Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux
• Abstract summary: Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. Traditional actor-critic algorithms do not succeed in fitting the true value function. We provide a new state-value (resp. state-action-value) function approximation that learns the value of the states relative to their mean value.
• Score: 22.414430270991005
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issues. In this paper, we address these challenges by providing a different approach for training the critic in the actor-critic framework. Our work builds on recent studies indicating that traditional actor-critic algorithms do not succeed in fitting the true value function, calling for the need to identify a better objective for the critic. In our method, the critic uses a new state-value (resp. state-action-value) function approximation that learns the value of the states (resp. state-action pairs) relative to their mean value rather than the absolute value as in conventional actor-critic. We prove the theoretical consistency of the new gradient estimator and observe dramatic empirical improvement across a variety of continuous control tasks and algorithms. Furthermore, we validate our method in tasks with sparse rewards, where we provide experimental evidence and theoretical insights.

Related papers

• Compatible Gradient Approximations for Actor-Critic Algorithms [0.0]
  We introduce an actor-critic algorithm that bypasses the need for such precision by employing a zerothorder approximation of the action-value gradient. Empirical results demonstrate that our algorithm not only matches but frequently exceeds the performance of current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-02T22:00:50Z)
• Learning Optimal Deterministic Policies with Stochastic Policy Gradients [62.81324245896716]
  Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. In common practice, convergence (hyper)policies are learned only to deploy their deterministic version. We show how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy.
  arXiv  Detail & Related papers  (2024-05-03T16:45:15Z)
• Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees [12.259191000019033]
  Actor-critic (AC) methods are widely used in reinforcement learning (RL) We design a joint objective for training the actor and critic in a decision-aware fashion. We empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.
  arXiv  Detail & Related papers  (2023-05-24T15:34:21Z)
• Solving Continuous Control via Q-learning [54.05120662838286]
  We show that a simple modification of deep Q-learning largely alleviates issues with actor-critic methods. By combining bang-bang action discretization with value decomposition, framing single-agent control as cooperative multi-agent reinforcement learning (MARL), this simple critic-only approach matches performance of state-of-the-art continuous actor-critic methods.
  arXiv  Detail & Related papers  (2022-10-22T22:55:50Z)
• Actor Prioritized Experience Replay [0.0]
  Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error. We introduce a novel experience replay sampling framework for actor-critic methods, which also regards issues with stability and recent findings behind the poor empirical performance of PER. An extensive set of experiments verifies our theoretical claims and demonstrates that the introduced method significantly outperforms the competing approaches.
  arXiv  Detail & Related papers  (2022-09-01T15:27:46Z)
• Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction [73.77593805292194]
  We train a separate exploration policy to maximize an approximate upper confidence bound of the critics in an off-policy actor-critic framework. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy actor-critic training.
  arXiv  Detail & Related papers  (2021-10-22T22:07:51Z)
• Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods [0.0]
  In value-based deep reinforcement learning methods, approximation of value functions induces overestimation bias and leads to suboptimal policies. We show that in deep actor-critic methods that aim to overcome the overestimation bias, if the reinforcement signals received by the agent have a high variance, a significant underestimation bias arises. To minimize the underestimation, we introduce a parameter-free, novel deep Q-learning variant.
  arXiv  Detail & Related papers  (2021-09-22T13:49:35Z)
• Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes [112.38662246621969]
  Reinforcement learning considers the problem of finding policies that maximize an expected cumulative reward in a Markov decision process with unknown transition probabilities. We compute unbiased navigation gradients of the value function which we use as ascent directions to update the policy. A major drawback of policy gradient-type algorithms is that they are limited to episodic tasks unless stationarity assumptions are imposed.
  arXiv  Detail & Related papers  (2020-10-16T15:15:42Z)
• How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization [10.424426548124696]
  We propose MAGE, a model-based actor-critic algorithm, grounded in the theory of policy gradients. MAGE backpropagates through the learned dynamics to compute gradient targets in temporal difference learning. We demonstrate the efficiency of the algorithm in comparison to model-free and model-based state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-04-29T16:30:53Z)
• Kalman meets Bellman: Improving Policy Evaluation through Value Tracking [59.691919635037216]
  Policy evaluation is a key process in Reinforcement Learning (RL) We devise an optimization method, called Kalman Optimization for Value Approximation (KOVA) KOVA minimizes a regularized objective function that concerns both parameter and noisy return uncertainties.
  arXiv  Detail & Related papers  (2020-02-17T13:30:43Z)
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning [70.01650994156797]
  Off- evaluation of sequential decision policies from observational data is necessary in batch reinforcement learning such as education healthcare. We develop an approach that estimates the bounds of a given policy. We prove convergence to the sharp bounds as we collect more confounded data.
  arXiv  Detail & Related papers  (2020-02-11T16:18:14Z)

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.