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Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas
TL;DR
Offline RL seeks to learn useful policies from fixed datasets because online interaction can be costly, unsafe, or undesirable, while standard off-policy methods often fail offline. The paper introduces critic-regularized regression, which filters dataset actions using critic information, and reports strong performance across challenging benchmark tasks, including high-dimensional settings.
Problem
Offline RL needs methods that learn from fixed data because online policy execution is often infeasible or undesirable, while naive off-policy algorithms frequently fail offline.
Method
CRR performs offline policy optimization as value-filtered regression, using critic information to select dataset actions and requiring minimal changes to actor-critic methods.
Results
CRR outperforms several state-of-the-art offline RL algorithms by a significant margin across a wide range of benchmark tasks and scales to high-dimensional state and action spaces.
Takeaways & Limitations
CRR provides a simple offline RL method that performs well on challenging tasks and datasets containing diverse or low-quality data.
Takeaways & Limitations
CRR performance depends on design choices such as the filter, and value overestimation can cause it to copy more inferior actions as training progresses.
Abstract
from arXiv · showhide
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and safety, both of which are particularly pertinent to real-world applications of RL. Unfortunately, most off-policy algorithms perform poorly when learning from a fixed dataset. In this paper, we propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR). We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces -- outperforming several state-of-the-art offline RL algorithms by a significant margin on a wide range of benchmark tasks.
1 Introduction
Offline RL learns policies from fixed logged data when online interaction is costly, unsafe, or ethically undesirable, but standard off-policy methods often fail because of optimistic Q-estimates and extrapolation beyond observed actions. CRR addresses this with value-filtered regression and performs strongly across challenging benchmark tasks.
- Motivation: Offline RL uses historical data to learn policies without further interaction with real systems, addressing cost, safety, and ethics concerns.Fixed datasets also help separate exploration from learning, supporting shared benchmarks and evaluation.
- Problem: Naive off-policy RL with function approximation often fails offline because Q-estimates become overly optimistic and generalize beyond observed data.Bootstrapping can query unobserved actions, allowing errors to accumulate.
- Approach: CRR reduces offline policy optimization to value-filtered regression with minimal changes to standard actor-critic methods.Its filtering compares dataset-action and policy-action values, retaining actions judged at least as valuable as the policy action.
- Results: CRR outperforms several state-of-the-art offline RL algorithms by a significant margin across a wide range of benchmark tasks.The method also scales to high-dimensional state and action spaces and performs well with diverse or low-quality data.
2 Related Work
Related offline RL methods address fixed-data failures through behavior cloning, distributional critics, action-support constraints, or advantage-weighted regression. CRR differs by estimating advantages without observed returns and by using its critic for test-time policy improvement.
- Behavior cloning: Behavior cloning trains a policy by supervised learning from state-action pairs, but its success depends on high-quality data.It can fail when data contains much random or task-irrelevant behavior or when the learned policy deviates from the dataset distribution.
- Off-policy RL: Off-policy deep RL can use rewards to outperform demonstrators, while distributional variants have shown effectiveness in some offline Atari and robot-manipulation tasks.Distributional methods nevertheless underperform BCQ in the cited comparison.
- Action constraints: Action-support methods constrain choices to training-data support using generative models, constrained optimization, or multiple Q-function estimates.These approaches target inappropriate generalization, overconfident Q estimates, and optimism bias.
- Weighted regression: Weighted behavior cloning selects dataset actions using estimated advantages, including MARWIL, BAIL, ABM, and advantage-weighted regression.CRR differs by avoiding observed returns for advantage estimation and introduces Critic Weighted Policy for test-time improvement.
- Filtered BC: Earlier filtered behavior-cloning methods were studied online, whereas CRR targets offline RL and combines filtering with additional design elements.The paper reports that filter choice, policy improvement, recurrent networks, and distributional value functions substantially affect offline results.
3 Critic Regularized Regression
CRR trains an offline policy by regressing toward dataset actions while filtering or weighting them with critic-derived value information. This restricts policy learning to observed actions and supports a regularized policy-improvement interpretation.
- Motivation: CRR addresses offline bootstrapping failures by avoiding Q evaluation for actions outside the training data.Standard policy evaluation may extrapolate to unseen actions, whose Q-values are untrained and unreliable.
- Policy learning: The CRR objective copies actions from the dataset, with a nonnegative weighting function f that increases monotonically with Qθ.Setting f := 1 recovers behavioral cloning, while value-dependent choices filter or emphasize better actions.
- Policy learning: Advantage-based filtering uses f := 1[Âθ(s, a) > 0], while exponential weighting uses advantage-scaled weights controlled by β.The filtering interpretation treats CRR as behavioral cloning on a dataset whose average action quality is increased.
- Policy learning: Exponential weighting approximately implements regularized policy improvement by sharpening the behavior-policy action distribution toward higher-value actions.As β →∞, the objective approaches behavior cloning.
- Theoretical analysis: In the tabular setting, CRR restricts action choices to the data support and can be interpreted as policy iteration that improves upon the behavior policy.This safety interpretation depends on the tabular analysis described in the appendix.
- Advantage estimation: CRR considers multiple advantage estimators, including sample-based mean and maximum estimates, with the maximum providing a more pessimistic alternative.Mean estimates may overestimate advantages for small sample counts because of stochasticity.
4 Experiments
The experiments evaluate CRR across diverse offline control, locomotion, and manipulation tasks, including high-dimensional actions and visual observations. CRR variants generally perform well, while filtering choices and baseline behavior vary with task difficulty and data quality.
- Environments and datasets: CRR is evaluated on challenging manipulation and locomotion domains, including high-dimensional action spaces and RGB-camera perception.The study also includes diverse datasets and ablations of individual algorithm components.
- Environments and datasets: Manipulation tasks use a 9DoF arm with proprioceptive features and two 64 × 64 camera views, with binary success rewards.Each task dataset comes from three independent DPGfD runs containing 8000 episodes each.
- Analysis of CRR variants: CRR evaluates exp, binary, and binary max variants; all perform reasonably across environments, with little difference on most tasks.The exp variant clips its weighting function at 20, while binary and binary max use different advantage estimates.
- Analysis of CRR variants: CRR binary and especially binary max outperform on low-complexity environments because their more aggressive filtering removes low-quality actions.The exp rule can copy sub-optimal actions when it is too permissive.
- Analysis of CRR variants: On harder environments, CRR exp is superior; on humanoid-run, binary retains 10%−12% of actions while binary max retains 2% −5%.The paper speculates that stricter filtering removes too many actions for learning difficult policies.
- Analysis of CRR variants: CWP generally improves CRR performance, so reported results use CWP for all CRR variants.The corresponding ablation is left to the appendix.
- Comparison to baselines: D4PG fails on higher-dimensional-action tasks, while BCQ fails on harder tasks such as humanoid-run and manipulator-insert-ball.ABM underperforms CRR on Humanoid Run and humanoid Locomotion tasks, whereas behavior cloning suffers with low-quality data.
5 Conclusion
CRR is presented as a simple offline RL algorithm that achieves strong performance on challenging tasks. The paper identifies filtering choices as important and highlights further study of their effects as future work.
- Conclusion: CRR is simpler than existing offline RL methods yet performs well on challenging tasks.The method is characterized as filtered behavioral cloning using information from the policy’s Q-function.
- Conclusion: CRR exp performs especially well across the full range of evaluated tasks.The evaluation finds that design factors such as filter choice can substantially affect performance depending on task nature.
- Conclusion: Further study of CRR’s underlying dynamics may support improvements in offline RL robustness, performance, and simplicity.The paper presents this as a direction for future work rather than an established result.
Broader Impact
The paper frames offline RL as a possible route toward beneficial real-world applications where online exploration is difficult or dangerous. It also warns that datasets and automated decision policies can create societal risks.
- Broader Impact: Offline RL could broaden practical applications of RL, including assistive robotic technologies, because online exploration is difficult or dangerous in the real world.The paper specifically connects this potential to applications beneficial to society.
- Broader Impact: Offline RL applications may have negative societal impacts, and dataset collection should address bias and prejudice.The warning applies to automated decision-making policies and the existing datasets they require.
A.1 Analysis of CRR in the tabular setting
In the tabular setting, CRR restricts learned policies to dataset-supported actions and improves performance over iterations, with empirical-MDP behavior becoming sensible as data grows.
- CRR is analyzed under finite state and action spaces with deterministic rewards in an underlying MDP.
- The empirical MDP is built from dataset transition counts and empirical policy frequencies, with an added terminal state for unseen transitions.
- Tabular CRR algorithms are presented as iterative procedures initialized with the empirical behavior policy and updated using one of two objectives.
- CRR policies do not select actions absent from the dataset’s empirical support.The tabular support guarantee holds for the exponential and binary objectives.
- Each CRR iteration improves performance in the tabular setting.The paper states this improvement for the tabular CRR analysis and gives separate binary and exponential policy-improvement propositions.
- As the dataset grows, Q-value differences between the ground-truth and empirical MDPs decrease, supporting sensible learned policies in the original environment.The asymptotic statement assumes suitable sampling conditions for state-action occurrences and transitions.
A.2 Evaluation protocol
Agent performance is estimated by evaluating saved snapshots across independent seeds and selecting the best snapshot’s mean episodic reward for each seed.
- Each agent is run with three independent seeds, and snapshots are saved every 50000 learner steps.
- For each agent, environment, and seed, every saved snapshot is evaluated for 300 episodes.
- The performance reported for a seed is the mean episodic reward of its best snapshot.
A.3 Effects of using K-step returns
The paper compares K-step returns with CRR’s proposed advantage estimation and reports that sufficiently large K-step horizons can bias learning and reduce performance.
- K-step returns may hurt performance because dataset transitions can come from policies unlike the current policy.Their returns may therefore fail to reflect the current policy’s actual returns.
- The evaluation tests CRR with the binary max rule while varying the advantage-estimation method.
- k = 5 produces degraded performance, supporting the claim that sufficiently large K-step returns introduce bias that compromises learning.
A.4 Hyper-parameters
This section describes the hyper-parameters used for the algorithms.
- The paper provides a section describing the algorithms’ hyper-parameters.
A.4.1 BCQ
The section reports implementation details and ablations for offline RL agents, including BCQ-compatible settings, value overestimation, policy noise, K-step returns, and CWP. These analyses identify conditions affecting policy performance and computation.
- BCQ implementation: BCQ uses the original network architecture and hyperparameters, with batch size increased to 1024 for compatibility with CRR.The implementation also shares policy-network structure and applicable hyperparameters with CRR across baseline methods.
- K-step returns: K-step returns with k = 5 degrade performance, consistent with bias compromising learning when k is sufficiently large.The figure evaluates K = 5 returns and reports that their use hurts policy performance.
- Value over-estimation: Dataset-action values can be overestimated relative to the policy, causing the binary max rule to copy more actions as agent performance improves.This counterintuitive pattern supports the explanation that some suboptimal dataset actions receive inflated values.
- Value over-estimation: Relative overestimation may cause CRR to clone suboptimal actions, while the binary max rule copies fewer such actions and can improve performance on some datasets.The binary max rule is described as optimistic about the policy’s state-value.
- Policy noise ablation: Turning off policy noise is usually harmless and often beneficial, with stronger effects in some humanoid locomotion environments where falls terminate episodes.In low- and moderate-dimensional environments, no noise is beneficial but often not significantly so.
- CWP ablation: CWP generally improves performance, especially for weaker policies, does not reduce performance when unhelpful, but requires additional computation.For mixture-of-Gaussians policies, CWP chooses among component-mean actions; the authors recommend it when execution cost is acceptable.