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S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Samarth Sinha, Ajay Mandlekar, Animesh Garg

arXiv:2103.06326v2cs.LG

TL;DR

Offline RL trains policies from fixed datasets, but neural Q-functions can overfit and generalize poorly beyond the collected data. S4RL applies state augmentations in Q-learning to smooth local value estimates; it reports significant performance gains on D4RL and dexterous robot-manipulation environments.

  • Problem

    Offline RL avoids further environment interaction, but Q-networks can overfit static data and generalize poorly to out-of-distribution states, while state augmentation for proprioceptive offline RL remains unclear.

    Source: §1 · p. 1

  • Method

    S4RL applies multiple state augmentations when calculating Q-values and targets, encouraging local consistency in Q estimates for Q-learning agents.

    Source: §4 · p. 4, §4.1 · p. 4

  • Results

    S4RL reports significant performance gains over base offline RL and augmentation baselines on D4RL and dexterous robot-manipulation environments.

    Source: §1 · p. 2, §6 · p. 8

  • Takeaways & Limitations

    The authors describe S4RL as simple to implement and addable to offline agents that use Q-learning.

    Source: §6 · p. 8

    State augmentation assumes locally smooth rewards, so transformations must remain local enough to preserve state semantics.

    Source: §4.2 · p. 5

Abstract

from arXiv · show

Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to learn useful skills from data that can then be deployed in the environment in real-world settings where interactions may be costly or dangerous, such as autonomous driving or factories. However, current algorithms overfit to the dataset they are trained on and exhibit poor out-of-distribution generalization to the environment when deployed. In this paper, we study the effectiveness of performing data augmentations on the state space, and study 7 different augmentation schemes and how they behave with existing offline RL algorithms. We then combine the best data performing augmentation scheme with a state-of-the-art Q-learning technique, and improve the function approximation of the Q-networks by smoothening out the learned state-action space. We experimentally show that using this Surprisingly Simple Self-Supervision technique in RL (S4RL), we significantly improve over the current state-of-the-art algorithms on offline robot learning environments such as MetaWorld [1] and RoboSuite [2,3], and benchmark datasets such as D4RL [4].

1 Introduction

S4RL combines state augmentations with Q-learning and significantly outperforms base offline RL and competing augmentation baselines. The introduction motivates this approach by offline RL’s generalization challenges and the open question of augmenting proprioceptive observations.

1 Introduction

  • Offline RL uses behavior-collected data to train a target policy without further environment interaction, which is useful when interaction is costly or unsafe.Behavior data may come from demonstrations, random policies, or near-optimal policies, and may not cover the full state-action space.Source: §1 · p. 1
  • Offline Q-networks can overestimate values for out-of-distribution data and overfit static datasets, while the role of state augmentation for proprioceptive offline RL remains unclear.Prior augmentation results cited in the paper concern pixel-based methods and standard reinforcement learning.Source: §1 · p. 1
  • S4RL applies multiple state augmentations to calculate Q-values and targets, encouraging similar Q estimates for nearby state perturbations.The proposed smoothing addresses the concern that small observation changes should not produce drastically different Q-values.Source: Figure 1 · p. 2, §1 · p. 2
  • S4RL significantly outperforms base offline RL and competing augmentation baselines across the reported evaluations.The paper evaluates on D4RL, MetaWorld, and RoboSuite.Source: §1 · p. 2

2 Related Work

Prior offline RL work addresses out-of-distribution value errors through policy constraints, pessimism, and other approaches, while representation-learning research explores augmentation and self-supervision. S4RL adapts augmentation to proprioceptive states for offline robotic tasks.

2 Related Work

  • Offline actor-critic methods such as SAC [16] can struggle with offline data because overestimation of unseen state-action values harms out-of-distribution generalization.Prior approaches include policy constraints using KL-divergence [10] [20], MMD [8], or f-divergence [21], state-conditioned VAEs [8] [22], pessimism [23], and Conservative Q-Learning [24].Source: §2 · p. 2
  • Prior offline robotic RL work uses large datasets [3] [30] [31] and policies that can be deployed after a few steps of finetuning.The paper also situates its work among offline RL methods using divergence constraints, VAEs, pessimism, conservative value estimates, and learned dynamics models [8] [10] [20] [21] [22] [23] [24] [25] [26].Source: §2 · pp. 2–3
  • Pixel-based RL work used augmentation and contrastive learning, while RAD [15] and DrQ [14] showed augmentation can be used without a contrastive objective.S4RL builds on augmentation-based Q-learning but studies offline robotic tasks and state-based augmentation.Source: §2 · p. 3

3 Preliminaries

The preliminaries frame reinforcement learning as optimizing a policy in an MDP, then contrast offline learning from a fixed behavior dataset with interactive training. The section also introduces value estimation and the smoothing illustrated by augmented Bellman backups.

3 Preliminaries

  • An MDP is represented by state and action spaces, a transition function, reward model, discount factor, and initial-state distribution.The discount factor γ lies in [0, 1).Source: §3 · p. 3
  • A policy maps states to actions and is trained to maximize expected cumulative discounted reward; Q(st, at) gives the value of taking action at in state st.The Q-function is fitted by minimizing Bellman error during policy evaluation.Source: §3 · p. 3
  • Policy improvement trains the policy to maximize state-action values, whereas offline RL trains from a static dataset collected by a behavior policy.The behavior policy may combine suboptimal policies, and offline methods can generalize poorly to out-of-distribution data; typical methods perform poorly on mixtures of suboptimal or random-policy data [4].Source: §3 · p. 3
  • The Figure 2 comparison depicts augmented Bellman backups as smoothing the local state-space around dataset observations.It contrasts ordinary offline backups with backups using data augmentations.Source: Figure 2 · p. 4

4 Method

S4RL uses multiple state augmentations to smooth Q-learning over nearby states in offline RL. Its approach assumes local perturbations preserve reward semantics and should remain physically realizable.

4.1 Self-Supervision in Offline RL

  • S4RL averages state-action values and target values across augmented states, using a mean Bellman error to encourage consistent Q-values around each state.The policy is improved without augmentations, distilling the value functions’ benefits into the policy.Source: §4.1 · p. 4

4.2 Data Augmentations

  • Offline augmentation enables local exploration from dataset trajectories, but transformations should be physically realizable and preserve locally smooth rewards.Aggressive perturbations may change rewards, so augmented-state rewards are assumed to remain close to the original state’s reward.Source: §4.2 · p. 5
  • The seven augmentation strategies include Gaussian and uniform noise, which perturb states additively.Gaussian noise is sampled from N(0, σI); uniform noise is sampled from U(−α, α).Source: §4 · p. 4, §4.2 · p. 5
  • The remaining strategies scale state dimensions, drop one random dimension, or switch two dimensions using environment-specific pairs.State-switching can violate physical realizability when the selected dimensions are semantically dissimilar.Source: §4.2 · p. 5
  • Other strategies include mix-up between current and next states and adversarial perturbations directed toward Q-value deviation.Mix-up leaves the next state unchanged; adversarial training selects a direction within an ε-ball of the current state.Source: §4.2 · p. 5

5 Experiments

The experiments benchmark state augmentations and S4RL against offline-RL and self-supervision baselines across D4RL and robotic manipulation tasks. S4RL improves results across these settings, though Dimension-Dropout and State-Switch underperform CQL.

5 Experiments

  • The experiments compare augmentations on D4RL, then test selected variants against self-supervision baselines and add S4RL to CQL and BRAC.The study also evaluates difficult robotic manipulation tasks; base CQL hyperparameters are not tuned.Source: §5 · p. 6

5.1 Results

  • Zero-mean Gaussian and Uniform noise, state mix-up, and adversarial training consistently outperform CQL; S4RL-Adv and S4RL-N rank 1.83 and 1.92 on average.CQL+S4RL also learns useful policies on walker-random, where base CQL learns no meaningful policy.Source: §5.1 · p. 6, Table 1 · p. 7
  • On medium-replay, S4RL significantly outperforms CQL; this split includes data collected during policy training, ranging from random to medium policies.Source: §5.1 · pp. 6–7
  • Across D4RL, S4RL-N and S4RL-Adv outperform baselines on almost all tasks and are comparable to the best elsewhere; S4RL-Adv excels in high-dimensional and AntMaze settings.The comparison covers CQL and BRAC; the reported full-set results are mean normalized performance over five random seeds.Source: Table 2 · p. 7, §5.1 · p. 8
  • Dimension-Dropout and State-Switch perform worse than CQL; the authors suggest element-wise edits may omit important state information such as joint velocity.That missing information may leave the value function unable to reason about the environment and produce poor value estimates.Source: §5.1 · p. 7
  • Figure 4 reports approximately 20% higher policy success for S4RL augmentation than base CQL across MetaWorld and RoboSuite tasks.The figure measures the fraction of goals reached during evaluation.Source: Figure 4 · p. 8, §5.1 · p. 8
  • S4RL-Adv improves over the baseline in all seven robotic environments and exceeds the trained behavior policy on three of five MetaWorld tasks.On push-v1 and pick-and-place-can, it outperforms baselines by as much as three times.Source: §5.1 · p. 8

6 Conclusion

The conclusion presents S4RL as a simple, broadly applicable use of state data augmentation to improve Q-learning function approximation in offline RL. The reported gains span D4RL and dexterous robot manipulation, with future work proposed on using the augmentations in self-supervised learning.

  • S4RL uses data augmentation to improve function approximation for Q-learning algorithms in offline RL.Source: §6 · p. 8
  • The paper benchmarks seven state-augmentation strategies and reports significant gains on D4RL and dexterous robot manipulation environments.Source: §6 · p. 8
  • The authors suggest future work could use the proposed augmentations to build better self-supervised learning algorithms.Source: §6 · p. 8

A Learning with limited data

The limited-data experiments use subsets of D4RL data at three amounts across three data splits. On Hopper and Walker2d, S4RL-N outperforms all baselines in each tested split and data amount.

B Full Robotic Experiments

The section reports MetaWorld robotic-manipulation results and examines MC-Dropout and L2 regularization for Q-networks. The supplied passages describe the tables and experimental settings but not their numerical outcomes.

B Full Robotic Experiments

  • Table 3 reports the percentage of goals completed by the behaviour policy at 1M steps across MetaWorld robotic-manipulation environments [1].The caption says these numerical results are used in Figure 4 and identifies bold text as marking S4RL results that outperform baselines.Source: Table 3 · p. 12

C Q-network regularization

  • The regularization experiments compare MC-Dropout and L2 regularization over five difficult MetaWorld robotic tasks [1].The reported settings are p = 0.3 for Dropout and a weight decay value of 1 × 10−4 for L2 regularization.Source: C Q-network regularization · p. 12, Table 4 · p. 12
  • The section identifies Table 4 as the source of results for the two Q-network regularization methods.Source: C Q-network regularization · p. 12

D Ablation over Hyperparamaters

The hyperparameter ablation finds that S4RL performs best with local state perturbations. Large perturbations can make the new states semantically different from the original states and hurt performance.

D Ablation over Hyperparamaters

  • The reported settings are σ = 3 × 10^-4 for S4RL-N and ϵ = 1 × 10^-4 for S4RL-Adv.Table 5 presents the hyperparameter study on MetaWorld [1].Source: Table 5 · p. 12
  • Large σ or ϵ values of 10^-2 significantly hurt S4RL performance, while performance is relatively stable from 10^-3 to 10^-4.Source: D Ablation over Hyperparamaters · p. 12
  • Large variance or adversarial perturbations can produce states that are semantically different from the original proprioceptive state.The passage contrasts continuous proprioceptive inputs with pixel-based inputs.Source: D Ablation over Hyperparamaters · p. 12

E Implementation details on state-switch

State-switch augmentation replaces state dimensions using environment-specific rules. The method aims to preserve physically realizable bounds but requires knowledge of the state space and performs poorly on most baselines.

E Implementation details on state-switch

F Details about Robosuite experiments

The Robosuite Lift and Can datasets were collected from Soft Actor Critic agents trained from scratch. Regularly saved checkpoints supplied rollouts that combine expert and suboptimal trajectories.

F Details about Robosuite experiments

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