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An empirical investigation of the challenges of real-world reinforcement learning

Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

arXiv:2003.11881v2cs.LGcs.AI

TL;DR

Real-world RL adoption is slowed by a gap between experimental setups and the poorly defined realities of real systems. The paper formalizes nine challenges, studies their effects on RL agents, and provides benchmark tasks; explainability remains unaddressed in the suite.

  • Problem

    RL has worked in many simulated environments, but uptake in real-world problems has been slower amid a gap between experimental setups and real-system realities.

    Source: §1 · p. 1

  • Method

    The paper defines nine challenges, analyzes two state-of-the-art RL algorithms across them, and provides 12 combined-challenge benchmark tasks.

    Source: §1 · p. 2, §2 · p. 5, §5 · p. 34

  • Results

    Experiments show challenge-specific effects, including rapid performance degradation from observation and action delays; D4PG is slightly more robust to easy benchmark perturbations but otherwise quickly matches DMPO in poor performance, aside from quadruped:walk.

    Source: §5 · pp. 33–34

  • Takeaways & Limitations

    The realworldrl-suite provides reproducible experiments and reference tasks that researchers can replicate and extend for real-world RL research.

    Source: §5 · pp. 33–34

    The suite addresses eight of the nine proposed challenges; explainability remains for future iterations because objectively evaluating policy explainability is not trivial.

    Source: §2.11 · p. 28

Abstract

from arXiv · show

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. In this work, we identify and formalize a series of independent challenges that embody the difficulties that must be addressed for RL to be commonly deployed in real-world systems. For each challenge, we define it formally in the context of a Markov Decision Process, analyze the effects of the challenge on state-of-the-art learning algorithms, and present some existing attempts at tackling it. We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems. Our proposed challenges are implemented in a suite of continuous control environments called the realworldrl-suite which we propose an as an open-source benchmark.

1 Introduction

The paper attributes slow real-world RL uptake to a gap between experimental setups and poorly defined systems, and organizes the difficulty into nine challenges. It introduces examples of those challenges across application areas and proposes experiments, combined benchmark tasks, and an open-source suite.

1 Introduction

  • The authors formalize real-world RL difficulties as nine challenges, from limited samples and delays to constraints, partial observability, multi-objective rewards, offline learning, and explainability.They provide intuition about where challenges arise and discuss potential approaches in the literature.Source: §1 · p. 2

1.1 Illustrative Examples

  • Examples from robotics, healthcare, and software systems show challenges arising through limited data, delays, constraints, partial observability, and competing objectives.The examples are intended to illustrate how challenges manifest across varied application scenarios.Source: §1.1 · pp. 2–3

1.2 Contributions

  • The authors identify and define real-world challenges, design experiments to analyze them, define and baseline combined benchmark tasks, and open-source the environment suite.They aim to make the challenges identifiable and easier to study explicitly.Source: §1.2 · p. 3
  • The paper defines and implements environments for challenges except explainability, then trains two state-of-the-art agents across difficulty levels to analyze their effects.The analyses are used to compare challenge difficulty and calibrate challenge parameters.Source: §1.2 · pp. 3–4
  • On combined challenge tasks, state-of-the-art agents fail quickly even with mild perturbations along each challenge dimension.The authors combine a subset of challenges into calibrated benchmark tasks and baseline two learning agents.Source: §1.2 · p. 4
  • The open-source realworldrl-suite extends DeepMind Control Suite with parametrizable perturbations so researchers can test and extend experiments reproducibly.A user manual explains how to instantiate challenges and provides training examples.Source: §1.2 · p. 4

2 Analysis of the Real-World Challenges

The section formalizes real-world RL challenges in MDP terms and evaluates two model-free agents against them. Its analyses of limited samples and system delays show where learning performance and stability are challenged.

2 Analysis of the Real-World Challenges

  • The analysis formalizes each challenge, discusses potential approaches, and tests DMPO and D4PG on challenge environments using 30K episodes and five seeds.The paper also proposes combined reference challenges to emulate systems where multiple challenges occur together.Source: §2 · pp. 4–5

2.1 Challenge 1: Learning On the Real System from Limited Samples

  • DMPO has higher normalized regret than D4PG on all tasks, and D4PG is generally more sample efficient.Normalized regret measures return lost before convergence relative to the best known final policy.Source: §2.1 · pp. 6–7, Figure 1 · p. 7
  • After convergence, DMPO has higher instability than D4PG except on cartpole:swingup.Instability is the percentage of post-convergence episodes that are suboptimal.Source: §2.1 · p. 7, Figure 1 · p. 7

2.2 Challenge 2: System Delays

  • As action, observation, and reward delays increase, both algorithms’ performance decreases; reward delays affect them less than action or observation delays.Significant reward-delay drops appear only at delays up to 100 timesteps, while humanoid:walk degrades quickly under reward delay when action credit assignment is less obvious.Source: §2.2 · p. 8, Figure 2 · p. 8

2.3 Challenge 3: High-Dimensional Continuous State and Action Spaces

The suite tests how high-dimensional states, environmental constraints, sensor malfunctions, and changing dynamics affect continuous-control learning. Added state dimensions often preserve converged performance but can slow learning; constraint violations vary over time, while sensor faults and environmental perturbations can degrade performance or cause policy failure.

2.3 Challenge 3: High-Dimensional Continuous State and Action Spaces

  • State dimensionality and system stability both affect learning: quadruped converges faster than walker despite being higher-dimensional, while lower-dimensional cartpole outpaces humanoid.The baseline comparisons use unperturbed tasks.Source: §2.3 · p. 9
  • Added dummy state dimensions preserve optimal converged performance on cartpole, quadruped, and walker, but can slightly slow walker learning.The added dimensions are zero-mean, unit-variance noise; the reported slowdown concerns convergence speed.Source: §2.3 · p. 9, Figure 4 · p. 10, Figure 5 · p. 10

2.4 Challenge 4: Satisfying Environmental Constraints

  • The constraint experiments use binary safety constraints as passive behavior measures or agent observations, but the learner ignores them.A separate multi-objective task combines reward with a constraint-violation penalty.Source: §2.4 · pp. 11–12
  • As safety coeff approaches 0, constraint satisfaction becomes harder, and violations occur unevenly across an episode.In cartpole, pole, position, and acceleration constraints are more frequently violated as the cart swings back and forth.Source: §2.4 · p. 12, Figure 6 · p. 13

2.5 Challenge 5: Partial Observability and Non-Stationarity

  • For non-stationarity experiments, selected environment parameters are perturbed uniformly or cyclically, with sweeps ordered by increasing difficulty.Parameters are sampled or changed episode by episode; the selected parameters are those expected to affect the optimal policy.Source: §2.5 · p. 15
  • Stuck and dropped sensors significantly degrade final performance across the tested tasks.The experiments vary both the probability of a sensor malfunction and its duration; Gaussian action and observation noise are also tested.Source: §2.5 · p. 14, Figure 7 · p. 16, Figure 8 · p. 17
  • Varying environment parameters affects converged D4PG policies and, in many instances, causes them to fail completely.These perturbations are applied to policies trained without challenges in the environment.Source: §2.5 · p. 14, Figure 9 · p. 18
  • Increasing the perturbation range slows learning across all evaluated domains under both uniform and cyclic episode-wise perturbations.Uniform perturbations sample parameters from a distribution each episode; cyclic perturbations change parameters gradually and reset them at the upper limit.Source: §2.5 · p. 15, Figure 10 · p. 19, Figure 11 · p. 20

2.6 Challenge 6: Multi-Objective Reward Functions

The section examines how multi-objective rewards, real-time inference constraints, and offline logs affect RL agents. Its experiments expose trade-offs in task performance, action repetition, and offline learning across tasks.

2.6 Challenge 6: Multi-Objective Reward Functions

  • Prior approaches include per-objective value functions, Pareto-oriented policies, reward-mixture policies, CVaR, and inverse reward inference.The cited literature also discusses distributional RL and behavior conditioned on desired objective mixtures.Source: §2.6 · p. 21
  • Evaluating each sub-reward separately reveals policy trade-offs that a single global reward can conceal.The proposed analysis represents a policy’s return as a vector of per-component rewards.Source: §2.6 · p. 21
  • As constraint reward weight rises, constraints are quickly satisfied, but over-emphasizing it degrades base-task performance.Figure 12 tracks original task reward and constraint violations as the reward-mixture coefficient changes.Source: §2.6 · p. 22, Figure 12 · p. 23

2.7 Challenge 7: Real-time Inference Challenge

  • The suite models inference constraints through action delay for latency and repeated actions for limited throughput.With repetition length k, the agent chooses an action every k steps and repeats it for the intervening steps.Source: §2.7 · p. 24
  • As repeated-action counts increase, agent performance generally deteriorates; quadruped is more robust than cartpole and walker despite larger state and action spaces.The authors suggest differing task stability may explain the contrast, with humanoid least stable and quadruped most stable.Source: §2.7 · p. 24, Figure 13 · p. 25

2.8 Challenge 8: Offline Reinforcement Learning - Training from Offline Logs

  • Offline tests use datasets from sub-optimal behavior policies and compare fixed-buffer D4PG with CRR, which imitates behavior actions having positive advantage.The combined-challenge offline task uses the largest dataset setting to keep it solvable; humanoid behavior data was too poor for that evaluation.Source: §2.8 · pp. 25–26
  • Offline results are uneven: CRR performs decently on walker and quadruped but fails to learn properly on humanoid and cartpole, while D4PG fails across reported cases.Dataset size shows no significant performance effect in the no-challenge setting; on combined-challenge quadruped CRR learns sub-optimally, whereas offline D4PG fails.Source: §2.8 · p. 26

2.9 Summarizing the overall performance of an agent

The paper proposes visual summaries of performance across challenge dimensions and combines calibrated challenges into benchmark tasks. It also identifies explainability and broader infrastructural, societal, and problem-specific issues as remaining scope boundaries.

2.10 Combining the Challenges: RWRL Benchmark

  • The calibrated combined challenges are proposed as reproducible benchmarks, with naive-agent baselines intended as starting points for comparison.The suite loads the challenge settings directly, and the authors invite follow-up work on more performant algorithms.Source: §2.10 · p. 28

2.11 Future Iterations

  • The paper addresses eight of nine challenges; explainability remains for future work because objective evaluation is not trivial.The discussion links explainability to human operators’ need to understand policy intentions and failure origins.Source: §2.11 · pp. 28–29

2.11.1 Possible additions to the nine challenges

  • Evolving state or action spaces are proposed as an additional challenge, where adaptation may be preferable to retraining.Examples include new system features or new robot capabilities.Source: §2.11.1 · p. 29

2.11.2 Other challenges (e.g., infrastructure, societal etc)

  • Infrastructure, societal, and problem-dependent challenges are outside the paper’s scope, including compute allocation and determining RL suitability.The authors also cite accessible interfaces for people with limited RL knowledge.Source: §2.11.2 · p. 29

2.9 Summarizing the overall performance of an agent

  • Radar plots compare optimal performance with progressively harder settings across individual challenge dimensions, highlighting D4PG’s difficulty with hard settings.D4PG appears less sensitive to reward delay and Gaussian action noise on all domains except humanoid.Source: §2.9 · p. 26, Figure 16 · p. 30

2.10 Combining the Challenges: RWRL Benchmark

  • Both D4PG and DMPO lose performance sharply when even mild versions of multiple challenges are combined.The combined benchmark includes perturbations such as delays, noise, action repetition, and stuck or dropped sensors.Source: §2.10 · p. 28, Table 7 · p. 29, Table 8 · p. 29

3 Additional Related Work

The related work spans practical RL systems, benchmark suites, and real-world applications. It positions the proposed challenges alongside complementary frameworks and reports prior robotic successes.

3 Additional Related Work

  • The authors frame their work as a response to limited real-world application evidence and calls for reproducible, practically relevant RL evaluation.They connect this motivation to Wagstaff [2012] and Henderson et al. [2018].Source: §3 · p. 32
  • Hester and Stone [2013] focus on four robotic challenges, while bsuite [Osband et al. 2019] targets fundamental RL problems and Safety Gym adds planar safety constraints.The authors describe their suite as offering more varied constraints and custom constraint support than Safety Gym.Source: §3 · p. 32
  • Horizon [Gauci et al. 2018] and Decision Service [Agarwal et al. 2016] provide RL training, evaluation, and deployment platforms; Ahn et al. [2019] present realistic robot designs and simulators.Decision Service logs transition probabilities to support later off-policy evaluation.Source: §3 · p. 32
  • Prior real-world RL examples include robot manipulation and grasping, with Gu et al. (2017), QT-Opt [Kalashnikov et al. 2018], and Levine et al. [2016] among the cited systems.The examples include generalization to unseen objects and coordinated vision-to-torque manipulation.Source: §3 · p. 32

4 Challenge Suite Overview

The realworldrl-suite packages configurable challenge environments, benchmark combinations, and tools for running and plotting agents. Its evaluation uses selected control tasks with adjustable challenge difficulty.

4 Challenge Suite Overview

  • The open-source suite includes seven challenge wrappers across eight DeepMind Control Suite tasks.Tasks cover cartpole, walker, quadruped, and humanoid variants.Source: §4 · p. 32
  • Users can configure challenge variants, combine them through a simple configuration language, and instantiate easy, medium, or hard combined challenges.The suite also provides runnable examples, a browser-based Jupyter notebook, and plotting functions.Source: §4 · p. 33
  • Evaluation uses four tasks chosen for varied difficulty and dimensionality, with parameterized wrappers that scale challenge effects from absent to very difficult.The suite aims to reproduce complex-system difficulties in a simplified setup for principled research.Source: §4 · p. 33

5 Discussion & Conclusion

The paper defines real-world RL challenges, studies their effects on common agents, establishes combined-challenge benchmarks, and releases an environment suite for comparison and extension.

5 Discussion & Conclusion

  • The paper's contributions include defining challenges, analyzing agents, benchmarking combined tasks, and open-sourcing realworldrl-suite for reproducing and extending experiments.The suite provides reference implementations and supports comparisons against common baselines.Source: §5 · pp. 33–34
  • The authors emphasize offline learning from logs where exploration is poorly tolerated, and argue that reasoning about environmental constraints may bring RL to fragile or expensive systems.They identify datacenter cooling and robotics as examples of settings with logs but little tolerance for exploratory actions.Source: §5 · p. 33
  • DMPO was almost an order of magnitude worse in performance regret but significantly more stable once converged; observation and action delays quickly degrade performance, while added observation dimensions had little effect.Environments with more action dimensions were not necessarily harder to learn.Source: §5 · p. 33
  • Constraints can be violated across task regimes, stuck sensors pose less of a problem than dropped signals, and non-stationary dynamics affect naive policies more quickly.The effect of changing dynamics depends on which element varies.Source: §5 · p. 34
  • Misaligned multi-objective rewards can degrade performance, and real-time agents degrade when system control frequency exceeds response time and actions decorrelate from observations.Simply applying D4PG to logged data was insufficient for a functional policy, while offline-specific algorithms handled even small datasets.Source: §5 · p. 34
  • The combined-challenge suite contains 12 benchmark tasks across three difficulty levels and four task domains; D4PG was slightly more robust to easy perturbations but usually matched DMPO's poor performance.Quadruped:walk was the stated exception to the D4PG–DMPO performance pattern.Source: §5 · p. 34

A Learning Algorithms

The appendix identifies where the D4PG and DMPO hyperparameters are reported.

A Learning Algorithms

B Parameters

The appendix points readers to the challenge-specific hyperparameter sweeps.

B Parameters

  • Hyperparameter sweeps for each challenge experiment are reported in Table 11.The passage directs readers to Table 11 for the individual-challenge sweeps.Source: B Parameters · p. 44, Table 11 · p. 44

C.1 Specifying Challenges

Challenge settings are passed as arguments to the environment's load method, with specifications for noise, perturbations, dimensionality, multi-objective rewards, constraints, and delays.

C.1 Specifying Challenges

  • The environment's load method accepts challenge arguments, and the suite documents the available configuration fields for replicating task settings.The argument definitions are presented as specifications for configuring the task.Source: §C.1 · pp. 44–46
  • Constraint settings define safety constraints and limits, scale them with a safety coefficient, and optionally add constraint-satisfaction indicators to observations.The implementation returns a Boolean observation entry for each constraint when these indicators are enabled.Source: §C.1 · p. 44
  • Delay settings specify the number of steps for action execution and for the freshness offsets of returned observations and rewards.The delay specification contains separate action, observation, and reward values.Source: §C.1 · p. 45
  • Noise specifications configure additive Gaussian noise, dropped or stuck values, and repeated actions, including probabilities, durations, and action or observation targets.The noise specification is passed as a dictionary to the task configuration.Source: §C.1 · p. 45
  • Perturbation settings select a parameter, update frequency, scheduler, and bounds or starting value to control non-stationary changes.Supported schedulers include random walks, drift, cyclic, uniform, and saw-wave patterns.Source: §C.1 · p. 46
  • Dimensionality settings add random observations, while multi-objective settings define objectives, their reward mix, and whether objectives enter observations.The multi-objective configuration can add its reward to the environment reward and renormalize it to [0,1].Source: §C.1 · p. 46

C.2 Code Snippets

The code snippets show how to load an environment for the OpenAI PPO baseline, instantiate an environment with the easy challenge, and run a random policy.

C.2 Code Snippets

  • The PPO example imports the baseline and helper modules, then defines a function to load an environment from the suite.The environment kwargs enable safety-variable logging and flat observations.Source: §C.2 · p. 47
  • The random-policy example runs episodes and reports mean and standard deviation of total reward per episode.It accumulates rewards until each episode ends, then summarizes the episode returns.Source: §C.2 · pp. 47–48
  • A separate example instantiates an environment with the easy challenge and enables safety-variable logging and flat observations.The snippet imports the suite’s environment module and sets both options in the environment kwargs.Source: §C.2 · p. 48

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