Source-linked AI summary
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, Ofir Nachum
TL;DR
Offline RL must learn from fixed datasets when online interaction is costly or unsafe, yet standard algorithms can struggle with this constraint. The paper introduces BRAC to compare behavior-regularized methods and design choices across offline continuous-control tasks. It finds that strong performance does not require several sophisticated techniques, while ablations identify differences in regularization and sensitivity.
Problem
Offline RL lacks further environment interaction, and standard algorithms can perform poorly when learning from fixed datasets, motivating evaluation of proposed remedies and design choices.
Method
BRAC is a modular actor-critic framework that regularizes learned policies toward the behavior policy while varying value penalties, policy penalties, divergences, and training techniques.
Results
Many sophisticated techniques, including weighted target Q-value ensembles and adaptive regularization coefficients, are not necessary for strong or state-of-the-art performance; value penalties are slightly better than policy regularization, while several divergences perform similarly.
Takeaways & Limitations
Simple behavior-regularized variants can perform well, and ablations provide insights into design choices affecting offline-RL performance and robustness.
Takeaways & Limitations
Learned Q-values are not a reliable proxy for selecting hyperparameters or policies without direct environment testing, leaving hyperparameter selection for future work.
Abstract
from arXiv · showhide
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment. However in many real-world applications, access to the environment is limited to a fixed offline dataset of logged experience. In such settings, standard RL algorithms have been shown to diverge or otherwise yield poor performance. Accordingly, recent work has suggested a number of remedies to these issues. In this work, we introduce a general framework, behavior regularized actor critic (BRAC), to empirically evaluate recently proposed methods as well as a number of simple baselines across a variety of offline continuous control tasks. Surprisingly, we find that many of the technical complexities introduced in recent methods are unnecessary to achieve strong performance. Additional ablations provide insights into which design choices matter most in the offline RL setting.
1 Introduction
Offline RL learns from a fixed dataset without further environment interaction, creating challenges for standard algorithms and motivating behavior-regularized approaches. BRAC evaluates which design choices are necessary and finds that simple variants can perform well without several sophisticated techniques.
- Motivation: Offline RL restricts learning to a fixed experience dataset, which is important when online deployment is costly or safety-sensitive.Applications may permit policy deployment only infrequently after extensive testing.
- Motivation: Function approximation and dataset-distribution sensitivity make offline learning challenging for continuous or large state-action spaces.These issues affect off-policy algorithms including DQN, DDPG, and SAC.
- Prior approaches: Recent remedies stabilize offline learning or regularize policies toward behavior-policy support, but introduce multiple interacting design modifications.Examples include randomized Q-target ensembles and behavior-policy regularization.
- Contribution: BRAC provides a modular framework for comparing behavior-regularized approaches and their algorithmic building components in continuous-action offline RL.The framework encompasses several recently demonstrated approaches.
- Findings: Simple behavior-regularized variants can achieve good performance, while weighted Q-ensembles and adaptive regularization weights are not crucial.Ablations further examine how design choices affect performance and robustness.
2 Background
The paper frames RL through MDPs, value functions, and actor-critic updates, then specializes the setting to offline learning from fixed transitions. Prior continuous-control methods constrain learned policies toward behavior-policy support using regularization or parameterization.
- Markov Decision Processes: An MDP comprises states, actions, transitions, rewards, and a discount factor; the objective is maximizing cumulative discounted reward.Policies induce multi-step state-transition distributions.
- Actor-Critic Methods: Actor-critic methods alternate learning a Q-function from Bellman errors and updating the policy to maximize estimated Q-values.Target Q-functions are commonly formed with slowly updated parameters.
- Offline Reinforcement Learning: Offline RL learns from fixed single-step transitions, while the behavior policy is defined as the dataset’s conditional action distribution.When the behavior policy is unavailable directly, prior work commonly estimates it by maximum likelihood.
- Prior Methods: BEAR penalizes policy divergence from behavior-policy support using kernel MMD and uses target-Q techniques intended to limit overestimation.Its implementation uses a soft-updated target-Q ensemble of size k = 4, although the ensemble-variance penalty is omitted because empirical results show no clear benefit.
- Prior Methods: BCQ keeps the learned policy close to the behavior policy through a specific parameterization rather than explicit regularizers.Its policy optimization can be viewed as restricted optimization over parameterized policies.
3 Behavior Regularized Actor Critic
BRAC is a modular actor-critic framework that regularizes learned policies toward the behavior policy while varying value penalties, policy penalties, divergences, and Q-target estimation. The framework supports implementations of existing methods and enables systematic ablations of these choices.
- Framework: BRAC generalizes behavior-regularized offline RL approaches while allowing different implementation choices to be compared within one framework.The framework is designed to evaluate which design choices matter in offline RL.
- Value Penalty: Value-penalty BRAC adds a behavior-policy divergence term to the target Q-value calculation.The divergence may be estimated from action distributions, including MMD or KL divergence.
- Policy Regularization: Policy-regularization BRAC sets the Q-update penalty to zero and applies the behavior-policy penalty only during policy optimization.This variant alternates actor and critic updates using the corresponding objectives.
- Divergences: BRAC supports multiple divergence estimators, including kernel MMD, KL divergence, and Wasserstein distance.MMD requires a pre-estimated cloned behavior policy, whereas dual f-divergence and Wasserstein forms use a learned discriminator.
- Existing Methods: Existing methods can be instantiated in BRAC, including BEAR with MMD and adaptive regularization and BCQ with restricted policy optimization.This makes their design choices directly comparable under the common framework.
4 Experiments
BRAC evaluates behavior-regularized offline RL design choices on fixed datasets from four continuous-control environments. The experiments find that several simple choices perform strongly, while some sophisticated mechanisms add little benefit and sensitivity varies across variants.
- Experimental setup: Experiments use four continuous-control environments and five fixed datasets per environment generated from noisy partially trained policies.The environments are Ant-v2, HalfCheetah-v2, Hopper-v2, and Walker2d-v2.
- Regularization weights: Fixed regularization weights are simpler and perform better than adaptively trained weights in the initial BEAR experiments.The learned adaptive weight consistently increased, suggesting the MMD constraint was almost never satisfied.
- Target Q-values: Using an ensemble of k > 1 Q-functions improves over a single Q-function, while k = 4 provides only marginal gains over k = 2 on these domains.The authors therefore use k = 2 in subsequent experiments.
- Target Q-values: Taking the minimum of two Q-functions is slightly better than a weighted mixture except in Hopper-v2, while both outperform the partially trained policy.The minimum is used subsequently because of its simplicity and strong performance.
- Regularization placement: Value penalty usually performs slightly better than policy regularization, and this advantage persists across other divergence choices.Both variants outperform the partially trained policy.
- Divergences and baselines: All tested divergences perform similarly, but dual-form divergences are more hyperparameter-sensitive and value penalty is slightly more sensitive than policy regularization.The divergence variants significantly improve over the partially trained policy, while vanilla SAC, behavior cloning, and BCQ show weaker comparative performance in the reported baselines.
5 Conclusion
BRAC generalizes behavior-regularized offline RL and finds that simpler variants can achieve strong performance without several sophisticated training techniques. Proper hyperparameter selection remains central, while offline policy selection and smaller datasets remain open challenges.
- BRAC generalizes existing behavior-regularized offline RL approaches by regularizing learned policies toward the behavior policy.
- Weighted target Q-value ensembles and adaptive regularization coefficients are not necessary for state-of-the-art performance in the experiments.
- Value penalty is slightly better than policy regularization, while KL, MMD, and Wasserstein divergences achieve similar performance.
- Proper hyperparameters are a key differentiator, although some BRAC variants are more robust to hyperparameter choices than others.
- Off-policy evaluation without environment interaction and reliable training on smaller datasets remain open directions for truly offline RL.
A.1 Additional experiment details
The experiments use noisy offline datasets from continuous-control tasks and select hyperparameters through grid searches over learning rates and regularization controls. Implementation details vary across algorithms but share common neural-network, optimization, and training settings.
- Dataset collection: Each environment contributes five 1-million-transition datasets generated from a partially trained policy with no noise, epsilon-greedy noise, or Gaussian action noise.
- Hyperparameter search: For each BRAC variant and environment, the search covers six policy learning rates and five values for the algorithm-specific regularization control.
- Hyperparameter search: BCQ tunes perturbation range Φ, whereas BEAR tunes divergence constraint ϵ for adaptive regularization.
- Hyperparameter search: The best hyperparameters are selected using average performance across all five datasets, including the reported values from prior algorithm papers in the search ranges.
- Implementation details: Experiments use fully connected ReLU networks, Adam optimization, target Q-functions, 0.5 million training steps, and batch size 256, except BCQ uses batch size 100.
A.2 Value penalty v.s. policy regularization
Across all four divergences, value penalty generally performs slightly better than policy regularization.
- Value penalty is usually slightly better than policy regularization across all four divergences.
A.3 Full performance results under different hyperparameters
The figures visualize performance under different hyperparameters, including averages over all five datasets. These plots support comparing hyperparameter sensitivity across settings.
- Performance is visualized under different hyperparameters and averaged over all five datasets in Figures 9 and 10.
- Figure 11 visualizes performance under different hyperparameters without a stated dataset-averaging specification.
A.4 Additional training curves
Additional training curves compare KL-based value penalization with baselines and examine different divergences under policy regularization and value penalties. The divergence comparisons show similar performance across divergences.
- Training curves compare kl_vp with other baselines across all five datasets.
- With policy regularization, the different divergences perform similarly.
- With value penalties, the different divergences also perform similarly.
A.5 Full performance results under the best hyperparameters
The full performance results report evaluations under tuned hyperparameters across four tables. Table 1 additionally defines zero performance as an overflow caused by diverging Q-functions during training.
- Table 1 reports evaluation results with tuned hyperparameters and identifies zero performance as training overflow from diverging Q-functions.
- Table 2 reports evaluation results obtained with tuned hyperparameters.
- Table 3 reports evaluation results obtained with tuned hyperparameters.
- Table 4 reports evaluation results obtained with tuned hyperparameters.