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Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization

This repository contains code for the RLC'24 paper "Dreaming of Many Worlds: Learning Contextual World Models aids Zero-Shot Generalization".

If you use our work in you research or application, please cite us:

@article{prasanna-rlc24a,
  title = {Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization},
  author = {Prasanna, Sai and Farid, Karim and Rajan, Raghu and Biedenkapp, André},
  year = {2024},
  journal = {Reinforcement Learning Journal},
  volume = {1},
  issue = {1},
}

Code

The training and evaluation code for our experiments is at contextual_mbrl directory. The contextual_mbrl/dreamer/envs.py defines all our context variations for train and eval and contextual_mbrl/dreamer/configs.yaml defines all the configurations and hyperparameters for our runs. The contextual_mbrl/dreamer/record_dreams.py allows us to record the extrapolated/counterfactual dreams.

The code in dreamerv3_compat is taken from the fork which adds gymnasium support to the official DreamerV3 codebase. We changed it to incorporate our cRSSM method. Our changes are localized mainly to dreamerv3_compat/dreamerv3/nets.py and dreamerv3_compat/dreamerv3/agent.py.

Setup

Conda

First setup a miniconda environment.

conda env create --name c_mbrl --file environment.yml

Experiments

Main experiments

All our main experiments can be replicated by running sbatch 001_main_experiments.sh with slurm or modify it according to your training environment. To replicate individual experiments, you have to select one for each of the 4 main options.

  1. Task carl_classic_cartpole/carl_dmc_walker
  2. Modality img for pixel modality and obs for featurized modality
  3. Method enc_{$modality}_dec_{$modality}_pgm_ctx is the cRSSM setting, enc_{$modality}_dec_{$modality} is the hidden-context setting, enc_{$modality}_ctx_dec_{$modality}_ctx is the concat-context setting
  4. Training context default: Only on default context (makes sense to pair only with the hidden-context setting) single_0: vary the first context (gravity for cartpole and walker) single_0: vary the second context (length for cartpole and actuator strength for walker)

Then run the following commands your preferred settings and seed to run training followed by evaluation in required context regions.

python -m contextual_mbrl.dreamer.train --configs carl $scheme --task $task --env.carl.context $training_context --seed $seed --logdir logs/$experiment_name/$seed --wandb.project '' --run.steps $steps
python -m contextual_mbrl.dreamer.eval --logdir logs/$experiment_name/$seed

Experts and random policy

To train the experts, evaluate their mean returns and evaluate the models, run 002_cartpole_experts.sh and 003_walker_experts.sh.

An example to train cartpole export in gravity 17.64 and length 0.9 is

python -m contextual_mbrl.dreamer.train --configs carl enc_obs_dec_obs --task carl_classic_cartpole --env.carl.context specific_0-17.64_1-0.9  --seed 0 --logdir logs/specific/carl_classic_cartpole_specific_0-17.64_1-0.9/0 --wandb.project '' --run.steps 50000
python -m contextual_mbrl.dreamer.eval --logdir logs/specific/carl_classic_cartpole_specific_0-17.64_1-0.9/0
python -m contextual_mbrl.dreamer.eval --logdir logs/specific/carl_classic_cartpole_specific_0-17.64_1-0.9/0 --random_policy True

Dreams

The extrapolated and counterfactual dreams of the trained cartpole models can be obtained using 004_collect_dreams.sh. For individual recording of the dreams for a given context id (refer envs.py for context id to context mapping for different environments) for a given experiment run,

python -m contextual_mbrl.dreamer.record_dreams --logdir logs/$experiment_name/$seed --ctx_id 1

Use the counterfactual_ctx flag to provide counterfactual value and record dreams in different true contexts conditioning on this counterfactual value.

python -m contextual_mbrl.dreamer.record_dreams --logdir logs/$experiment_name/$seed --ctx_id 1 --counterfactual_ctx 1.0

Results

The figures can be plotted from the evaluations for all experiments using the notebooks plot_analysis.ipynb and plot_analysis_rliable.ipynb