The Ladder in Chaos: A Simple and Effective Improvement to General DRL
Algorithms by Policy Path Trimming and Boosting
- URL: http://arxiv.org/abs/2303.01391v1
- Date: Thu, 2 Mar 2023 16:20:46 GMT
- Title: The Ladder in Chaos: A Simple and Effective Improvement to General DRL
Algorithms by Policy Path Trimming and Boosting
- Authors: Hongyao Tang, Min Zhang, Jianye Hao
- Abstract summary: We study how the policy networks of typical DRL agents evolve during the learning process.
By performing a novel temporal SVD along policy learning path, the major and minor parameter directions are identified.
We propose a simple and effective method, called Policy Path Trimming and Boosting (PPTB) as a general plug-in improvement to DRL algorithms.
- Score: 36.79097098009172
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowing the learning dynamics of policy is significant to unveiling the
mysteries of Reinforcement Learning (RL). It is especially crucial yet
challenging to Deep RL, from which the remedies to notorious issues like sample
inefficiency and learning instability could be obtained. In this paper, we
study how the policy networks of typical DRL agents evolve during the learning
process by empirically investigating several kinds of temporal change for each
policy parameter. On typical MuJoCo and DeepMind Control Suite (DMC)
benchmarks, we find common phenomena for TD3 and RAD agents: 1) the activity of
policy network parameters is highly asymmetric and policy networks advance
monotonically along very few major parameter directions; 2) severe detours
occur in parameter update and harmonic-like changes are observed for all minor
parameter directions. By performing a novel temporal SVD along policy learning
path, the major and minor parameter directions are identified as the columns of
right unitary matrix associated with dominant and insignificant singular values
respectively. Driven by the discoveries above, we propose a simple and
effective method, called Policy Path Trimming and Boosting (PPTB), as a general
plug-in improvement to DRL algorithms. The key idea of PPTB is to periodically
trim the policy learning path by canceling the policy updates in minor
parameter directions, while boost the learning path by encouraging the advance
in major directions. In experiments, we demonstrate the general and significant
performance improvements brought by PPTB, when combined with TD3 and RAD in
MuJoCo and DMC environments respectively.
Related papers
- Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies [0.5919433278490629]
Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science.
Deep reinforcement learning (DRL) has the potential to solve high-dimensional and complex control problems.
In this work, we leverage dictionary learning and differentiable L$_0$ regularization to learn sparse, robust, and interpretable control policies for PDEs.
arXiv Detail & Related papers (2024-03-22T15:06:31Z) - Discovering Behavioral Modes in Deep Reinforcement Learning Policies
Using Trajectory Clustering in Latent Space [0.0]
We introduce a new approach for investigating the behavior modes of DRL policies.
Specifically, we use Pairwise Controlled Manifold Approximation Projection (PaCMAP) for dimensionality reduction and TRACLUS for trajectory clustering.
Our methodology helps identify diverse behavior patterns and suboptimal choices by the policy, thus allowing for targeted improvements.
arXiv Detail & Related papers (2024-02-20T11:50:50Z) - Statistically Efficient Variance Reduction with Double Policy Estimation
for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning [53.97273491846883]
We propose DPE: an RL algorithm that blends offline sequence modeling and offline reinforcement learning with Double Policy Estimation.
We validate our method in multiple tasks of OpenAI Gym with D4RL benchmarks.
arXiv Detail & Related papers (2023-08-28T20:46:07Z) - Hypernetworks for Zero-shot Transfer in Reinforcement Learning [21.994654567458017]
Hypernetworks are trained to generate behaviors across a range of unseen task conditions.
This work relates to meta RL, contextual RL, and transfer learning.
Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
arXiv Detail & Related papers (2022-11-28T15:48:35Z) - Mutual Information Regularized Offline Reinforcement Learning [76.05299071490913]
We propose a novel MISA framework to approach offline RL from the perspective of Mutual Information between States and Actions in the dataset.
We show that optimizing this lower bound is equivalent to maximizing the likelihood of a one-step improved policy on the offline dataset.
We introduce 3 different variants of MISA, and empirically demonstrate that tighter mutual information lower bound gives better offline RL performance.
arXiv Detail & Related papers (2022-10-14T03:22:43Z) - Jump-Start Reinforcement Learning [68.82380421479675]
We present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy.
In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks.
We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms.
arXiv Detail & Related papers (2022-04-05T17:25:22Z) - Policy Information Capacity: Information-Theoretic Measure for Task
Complexity in Deep Reinforcement Learning [83.66080019570461]
We propose two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty.
We show that these metrics have higher correlations with normalized task solvability scores than a variety of alternatives.
These metrics can also be used for fast and compute-efficient optimizations of key design parameters.
arXiv Detail & Related papers (2021-03-23T17:49:50Z) - FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance
Metric Learning and Behavior Regularization [10.243908145832394]
We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks.
This problem is still not fully understood, for which two major challenges need to be addressed.
We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches.
arXiv Detail & Related papers (2020-10-02T17:13:39Z) - Deep Reinforcement Learning using Cyclical Learning Rates [62.19441737665902]
One of the most influential parameters in optimization procedures based on gradient descent (SGD) is the learning rate.
We investigate cyclical learning and propose a method for defining a general cyclical learning rate for various DRL problems.
Our experiments show that, utilizing cyclical learning achieves similar or even better results than highly tuned fixed learning rates.
arXiv Detail & Related papers (2020-07-31T10:06:02Z)
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.