Task Domains¶

Here, we describe the various task domains available in DiscoBench. We expect this to continue to grow as our benchmark scales.

BayesianOptimisation¶

The agent must maximise randomly sampled variables using Bayesian Optimisation.

Modules¶

acq_fn, acq_optimizer, domain, next_queries, surrogate, surrogate_optimizer

Datasets¶

Ackley1d, Ackley2d, Branin2d, Bukin2d, Cosine8d, DropWave2d, EggHolder2d, Griewank5d, Hartmann6d, HolderTable2d, Levy6d

BrainSpeechDetection¶

The agent is tasked with training a speech detector based on brain MEG signals.

Modules¶

loss, networks, optim

Datasets¶

LibriBrainSherlock1, LibriBrainSherlock2, LibriBrainSherlock3, LibriBrainSherlock4, LibriBrainSherlock5, LibriBrainSherlock6, LibriBrainSherlock7

ComputerVisionClassification¶

The agent must train an image classifier for a range of different image classification datasets, of varying difficulty.

Modules¶

loss, networks, optim, preprocess

Datasets¶

CIFAR10, CIFAR10C, CIFAR10LT, CIFAR100, FashionMNIST, MNIST, OxfordFlowers, StanfordCars, TinyImageNet

ContinualLearning¶

The agent must train a model on different non-stationary continual learning tasks.

Modules¶

optim, regularizer, replay, sampler, scheduler

Datasets¶

PermutedMNIST, SplitCIFAR100, TinyImageNetSplit

GreenhouseGasPrediction¶

The agent must train a model to predict the changing concentrations of different greenhouse gases in the atmosphere.

Modules¶

data_processing, model

Datasets¶

CH4, CO2, N2O, SF6

LanguageModelling¶

The agent must pre-train a language model on different small-scale pretraining datasets.

Modules¶

loss, networks, optimizer

Datasets¶

LMFineWeb, OPCFineWebCode, OPCFineWebMath, TinyStories

ModelUnlearning¶

The agent must unlearn certain behaviours of a pretrained model while maintaining others.

Modules¶

loss

Datasets¶

muse, tofu, wmdp_cyber

Models¶

gemma-7b-it, Llama-2-7b-chat-hf, Llama-2-7b-hf, Llama-2-13b-hf, Llama-3.1-8b-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, phi-1_5, Phi-3.5-mini-instruct, Qwen2.5-1.5B-Instruct, Qwen2.5-3B-Instruct, Qwen-2.5-7B-Instruct

Installation¶

Please note, after installing the ModelUnlearning requirements.txt, you must install flash-attn. Please use:

pip install flash-attn==2.6.3 --no-build-isolation

OffPolicyRL¶

The agent must train a value-based RL agent in game environments.

Modules¶

config, networks, optim, policy, q_update, rb, train

Datasets¶

MinAtar/Asterix, MinAtar/Breakout, MinAtar/Freewar, MinAtar/SpaceInvaders

OnPolicyRL¶

The agent must train an on-policy RL agent in game and robotics environments.

Modules¶

config, networks, optim, train

Datasets¶

Brax/Ant, Brax/HalfCheetag, Brax/Hopper, Brax/Humanoid, Brax/Pusher, Brax/Reacher, Brax/Walker2D, Craftax/Craftax, Craftax/Craftax-Classic, MinAtar/Asterix, MinAtar/Breakout, MinAtar/Freewar, MinAtar/SpaceInvaders

UnsupervisedEnvironmentDesign¶

The agent must develop level sampling methods for an on-policy RL agent.

Modules¶

sample_levels, train_step, variable_config

Datasets¶

Kinetix/Large, Kinetix/Medium, Kinetix/Small, Minigrid