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Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt
TL;DR
RL research needs clear ways to test generalization, exploration, and long-term consequences while comparing agents scientifically. The paper introduces bsuite, a scalable collection of targeted experiments with open-source tooling for automated evaluation and analysis. Its experiments diagnose individual capabilities and make agent behaviour easier to compare, while the suite remains an evolving, non-exhaustive effort.
Problem
RL agents must address generalization, exploration, and long-term consequences, but researchers need clearer experiments for understanding and comparing these capabilities.
Method
The paper builds bsuite as a collection of targeted, scalable experiments with open-source code, reference agents, integrations, and automated evaluation and analysis.
Results
bsuite provides diagnostic experiments that isolate core capabilities, enforce evaluation methodology, vary complexity, and enable practical comparisons across RL agents.
Takeaways & Limitations
bsuite offers a reproducible and accessible way to make key RL issues concrete, testable, and informative for algorithm development.
Takeaways & Limitations
The current suite does not claim to capture all, or even most, important RL issues and notably omits targeted experiments for hierarchical reinforcement learning.
Abstract
from arXiv · showhide
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source github.com/deepmind/bsuite, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.
1 Introduction
bsuite is a collection of targeted, scalable experiments and open-source tools for diagnosing core RL capabilities and supporting reproducible comparisons. It complements existing benchmarks by emphasizing methodology, isolated capabilities, scalability, and accessibility rather than grand-challenge performance.
- Motivation: RL agents must simultaneously address generalization, exploration, and long-term consequences.These challenges concern learning efficiently from collected data, prioritizing useful experience, and considering effects beyond one timestep.
- Contribution: bsuite introduces carefully designed experiments that isolate core capabilities and make outstanding RL issues measurable and falsifiable.The suite is intended to bridge theory and practice by instantiating issues such as exploration and memory in testable experiments.
- Scope: bsuite is presented as an evolving research process rather than a complete collection of all important RL issues.The authors plan periodic review and invite the community to contribute excellent experiments in future releases.
- Novelty: Unlike grand-challenge benchmarks or leaderboards, bsuite uses diagnostic unit tests to provide insight into specific aspects of agent behaviour.Its experiments are analogous in spirit to clean, targeted tests such as MNIST, rather than integrated tests of general learning ability.
- Open source: The project open-sources code that instantiates experiments and automates agent evaluation and analysis to facilitate reproducible and accessible RL research.The release includes canonical experiments, reference algorithms, integrations with alternative codebases, automated analyses, and large-scale launch tools.
- Novelty: bsuite complements existing RL benchmarks by enforcing evaluation methodology, isolating capabilities, varying complexity smoothly, and supporting agents not designed specifically for bsuite.These design choices target scientific comparison and scalable analysis beyond fixed-size unit tests.
2 Experiments
bsuite defines reproducible diagnostic experiments that isolate core RL capabilities and evaluate agents through fixed environments, interactions, and analyses. The memory length and Deep Sea experiments illustrate how these tests expose scaling in memory and exploration.
- Experiment design: Each bsuite experiment fixes environments, an interaction regime, and an analysis procedure that maps agent behaviour to results and plots.
- Experiment design: Experiments target key RL issues while remaining simple, challenging, scalable, and fast enough to support repeated iteration.
- Memory length: Memory length tests how many sequential steps an agent can remember by varying maze length N and measuring regret after 10k episodes.
- Memory length: DQN and Bootstrapped DQN learn nothing for lengths > 1, whereas recurrent A2C performs well through N ≤30 before becoming essentially random beyond 30.
- Deep Sea: Deep Sea makes reward discovery exponentially difficult because dithering reaches the rewarding state with probability 2^-N per episode, while intermediate rewards favor the wrong direction.
- Deep Sea: Only Bootstrapped DQN scores well on Deep Sea and scales gracefully to large N, with learning-time progression suggesting possible scaling beyond N = 50.
3 How to use bsuite
bsuite supports reproducible RL evaluation through shared environments, automatic logging, notebooks, and diagnostic analyses. It helps researchers test capabilities quickly and communicate results consistently.
- bsuite provides high-level research and development use cases, while full installation and usage details remain in tutorials and appendices.
- Each experiment specifies environments and interaction episodes; loading environments automatically logs data for notebook-based analysis.
- The Figure 5 radar plot aggregates summary scores into a snapshot of seven core capabilities.
- Common code and analysis make agent benchmarking reproducible and verifiable beyond a single leaderboard score.
- bsuite acts as a diagnostic unit test for identifying capability bottlenecks faster and more cheaply than large-scale challenge runs.
- Automatic reports and online notebooks help disseminate results and support scientific evaluation of algorithmic changes.
4 Code structure
bsuite is implemented as a reusable library that supports standardized agent interfaces, automatic experiment handling, and compatibility with existing codebases. Its environments expose information needed for adaptive network construction and support multiple environment standards.
- The library is designed for RL research rather than as a standalone framework, with tutorials recommended directly from the open-source repository.
- Agents can implement policy and update methods, then use the run loop to execute experiments and automatically log results.
- Existing codebases can integrate bsuite by replacing the environment loader and iterating over bsuite identifiers.
- The 2019 release omits targeted experiments for hierarchical reinforcement learning and invites community contributions in that area.
- No individual bsuite environment takes more than 30 minutes, and the sweep can be parallelized across processes or machines.
- Observation specifications support adaptive network creation, while wrappers provide compatibility with dm env standards and OpenAI Gym.
5 Future iterations
bsuite is presented as an evolving research project whose experiment collection will be refined through community participation and periodic review. The project aims to use scalable experiments and accessible tools to improve understanding in RL research.
- The bsuite2019 release begins an ongoing effort to collect informative, targeted, scalable, and clear RL experiments.
- Future experiment submissions should include environment-selection sweeps, required logging, and analyses that parse the resulting data.
- bsuite aims to leverage large-scale computation for improved understanding rather than only improved performance.
- Accessible tools and reproducible experiments are intended to facilitate progress in reinforcement learning research.
A Experiment summary
The experiment summary begins with basic decision problems that test whether agents can learn rewarding policies. These experiments establish a general baseline competence rather than targeting specific RL issues.
- The appendix provides only outlines of the bsuite 2019 experiments, with full environment, interaction, and analysis documentation maintained in code.
- Basic experiments use simple decision problems and standard analysis to assess competence at learning a rewarding policy.
- These basic experiments test a general base level of competence expected from general agents rather than isolated core capabilities.
A.1.2 MNIST
This section describes contextual-bandit MNIST and Tetris environments used to test generalization and credit assignment. Both experiments run for 10k episodes and report average regret across 20 seeds.
- MNIST is a contextual-bandit classification task with ±1 rewards, evaluated over 20 seeds.
- The MNIST experiment runs for 10k episodes and records average regret.
- The MNIST task targets basic reinforcement learning and generalization.
- Tetris uses a 10x5 grid with one block falling per column, while the agent moves left or right along the bottom row to catch it.
- The Tetris experiment targets basic reinforcement learning and credit assignment.
A.2 Stochasticity
This section tests robustness to noisy rewards and reward-scale changes while motivating exploration as a tradeoff between information gathering and short-run performance.
- Reward noise: Reward-noise robustness is tested with Gaussian noise levels σ = [0.1, 0.3, 1, 3, 10] across 20 seeds.Four seeds are allocated to each noise level.
- Reward scale: Reward-scale robustness is tested by multiplying observed rewards by λ = [0.01, 0.1, 1, 10, 100] across 20 seeds.Four seeds are allocated to each reward-scaling level.
- Exploration: Exploration balances learning from poorly understood states and actions against exploiting existing knowledge for better short-run performance.
A.4.2 Stochastic deep sea
The section combines stochastic deep-sea and credit-assignment experiments with memory tests and the tooling used to summarize agent behaviour. These experiments evaluate regret, exploration, stochasticity, and memory across scalable settings.
- Stochastic deep sea: Stochastic deep-sea environments use stochastic transitions, N(0,1) reward noise, and sizes N=[5..50].
- Stochastic deep sea: The deep-sea experiment runs for 10k episodes, records average regret, and scores runs whose average regret is below 90% of random.
- Experiment targets: The deep-sea experiment targets exploration and stochasticity, whereas the umbrella problem targets credit assignment and stochasticity.
- Credit assignment: The umbrella problem makes only the first decision consequential and varies the number of confounding features from 1 to 100 logarithmically.
- Memory: Memory experiments use a T-maze whose length and number of bits to remember vary logarithmically, with 1k episodes and average-regret evaluation.
- Reporting: bsuite reports provide automatically generated summary plots and like-for-like comparisons across agents and frameworks.
C.3 Results commentary
The results commentary compares baseline agent behaviour across bsuite experiments and highlights distinct strengths in basic learning, exploration, and memory. DQN performs well on several task types but poorly on memory and exploration, while Bootstrapped DQN excels at exploration.
- Baseline behaviour: DQN performs well on basic tasks and quite well on credit assignment, generalization, noise, and scale.
- Baseline behaviour: DQN performs extremely poorly on memory and exploration tasks.The commentary attributes these weaknesses to a feedforward MLP without memory and inefficient ε=5%-greedy exploration.
- Baseline behaviour: Bootstrapped DQN is mostly identical to DQN except on exploration, where it greatly outperforms.The commentary links this pattern to uncertainty estimation and deep exploration.
- Baseline behaviour: Actor-critic RNN typically performs worse than DQN or Bootstrapped DQN on all tasks apart from memory.It is the only listed agent able to perform better than random on memory because of its recurrent architecture.
D.3 Results commentary
Across bsuite categories, adaptive optimizers outperform SGD, with the largest differences appearing on cartpole domains. The report evaluates agents through experiment scores and automated analysis.
- D.3 Results commentary: RMSProp and Adam outperform SGD in every category, while Adam usually slightly exceeds RMSProp.SGD performs particularly poorly on environments requiring generalization or scale.
- D.3 Results commentary: The optimizer differences are particularly pronounced on cartpole domains.The authors hypothesize that cartpole requires more efficient neural-network optimization and that SGD is prone to numerical issues.
- D.3 Results commentary: The evaluated Bootstrapped DQN agents use prior networks with ensemble sizes swept over {1, 3, 10, 30}.This experiment compares different instantiations of the default bsuite Bootstrapped DQN implementation.
- D.3 Results commentary: bsuite experiments produce summary scores in [0,1] that are aggregated by experiment type for standard analysis.The report describes automated evaluation and analysis of agents on these experiments.
E.3 Results commentary
Increasing Bootstrapped DQN ensemble size generally improves bsuite performance, with the strongest gains in exploration tasks. However, even large ensembles do not completely solve every cartpole swingup instance.
- E.3 Results commentary: Increasing ensemble size generally improves bsuite performance across the board.The gains show significantly decreasing returns, with ensemble size 30 not performing much better than size 10.
- E.3 Results commentary: Ensemble sizes below 10 cannot solve large deep sea tasks, whereas larger ensembles solve them reliably.The strongest gains from increasing ensemble size occur in exploration tasks.
- E.3 Results commentary: Large ensemble sizes still do not completely solve every cartpole swingup instance.The authors suggest that instability may contribute and that Double DQN could help address value overestimation.