p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] data/jasmine_data/atari/pong_no_sticky_actions.gin (plaintext)
# Hyperparameters follow Hessel et al. (2018)
import dopamine.jax.agents.rainbow.rainbow_agent
import dopamine.jax.replay_memory.replay_buffer
import dopamine.jax.replay_memory.samplers
import dopamine.discrete_domains.atari_lib
import dopamine.discrete_domains.run_experiment

JaxRainbowAgent.num_atoms = 51
JaxRainbowAgent.vmax = 10.
JaxRainbowAgent.gamma = 0.99
JaxRainbowAgent.update_horizon = 3
JaxRainbowAgent.min_replay_history = 20000  # agent steps
JaxRainbowAgent.update_period = 4
JaxRainbowAgent.target_update_period = 8000  # agent steps
JaxRainbowAgent.epsilon_train = 0.01
JaxRainbowAgent.epsilon_eval = 0.001
JaxRainbowAgent.epsilon_decay_period = 250000  # agent steps
JaxRainbowAgent.replay_scheme = 'prioritized'

# Note these parameters are different from C51's.
create_optimizer.learning_rate = 0.0000625
create_optimizer.eps = 0.00015

atari_lib.create_atari_environment.game_name = 'Pong'
# No sticky actions unlike suggested by prior work (Machado et al., 2017) since we want the actions to be ground-truth
atari_lib.create_atari_environment.sticky_actions = False
create_runner.schedule = 'continuous_train'
create_agent.agent_name = 'jax_rainbow'
create_agent.debug_mode = True
Runner.num_iterations = 200
Runner.training_steps = 250000  # agent steps
Runner.evaluation_steps = 125000  # agent steps
Runner.max_steps_per_episode = 27000  # agent steps

ReplayBuffer.max_capacity = 1_000_000
ReplayBuffer.batch_size = 32
PrioritizedSamplingDistribution.max_capacity = 1_000_000
[content] data/jasmine_data/atari/breakout_no_sticky_actions.gin (plaintext)
# Hyperparameters follow Hessel et al. (2018)
import dopamine.jax.agents.rainbow.rainbow_agent
import dopamine.jax.replay_memory.replay_buffer
import dopamine.jax.replay_memory.samplers
import dopamine.discrete_domains.atari_lib
import dopamine.discrete_domains.run_experiment

JaxRainbowAgent.num_atoms = 51
JaxRainbowAgent.vmax = 10.
JaxRainbowAgent.gamma = 0.99
JaxRainbowAgent.update_horizon = 3
JaxRainbowAgent.min_replay_history = 20000  # agent steps
JaxRainbowAgent.update_period = 4
JaxRainbowAgent.target_update_period = 8000  # agent steps
JaxRainbowAgent.epsilon_train = 0.01
JaxRainbowAgent.epsilon_eval = 0.001
JaxRainbowAgent.epsilon_decay_period = 250000  # agent steps
JaxRainbowAgent.replay_scheme = 'prioritized'

# Note these parameters are different from C51's.
create_optimizer.learning_rate = 0.0000625
create_optimizer.eps = 0.00015

atari_lib.create_atari_environment.game_name = 'Pong'
# No sticky actions unlike suggested by prior work (Machado et al., 2017) since we want the actions to be ground-truth
atari_lib.create_atari_environment.sticky_actions = False
create_runner.schedule = 'continuous_train'
create_agent.agent_name = 'jax_rainbow'
create_agent.debug_mode = True
Runner.num_iterations = 200
Runner.training_steps = 250000  # agent steps
Runner.evaluation_steps = 125000  # agent steps
Runner.max_steps_per_episode = 27000  # agent steps

ReplayBuffer.max_capacity = 1_000_000
ReplayBuffer.batch_size = 32
PrioritizedSamplingDistribution.max_capacity = 1_000_000
[tab] third_party/dopamine/docs/api_docs/python/dopamine/discrete_domains.md (markdown)
<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="dopamine.discrete_domains" />
<meta itemprop="path" content="Stable" />
</div>

# Module: dopamine.discrete_domains

<!-- Insert buttons and diff -->

<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
  <a target="_blank" href="https://github.com/google/dopamine/tree/master/dopamine/discrete_domains/__init__.py">
    <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
    View source on GitHub
  </a>
</td>
</table>

## Modules

[`atari_lib`](../dopamine/discrete_domains/atari_lib.md) module: Atari-specific
utilities including Atari-specific network architectures.

[`checkpointer`](../dopamine/discrete_domains/checkpointer.md) module: A
checkpointing mechanism for Dopamine agents.

[`gym_lib`](../dopamine/discrete_domains/gym_lib.md) module: Gym-specific
(non-Atari) utilities.

[`iteration_statistics`](../dopamine/discrete_domains/iteration_statistics.md)
module: A class for storing iteration-specific metrics.

[`logger`](../dopamine/discrete_domains/logger.md) module: A lightweight logging
mechanism for dopamine agents.

[`run_experiment`](../dopamine/discrete_domains/run_experiment.md) module:
Module defining classes and helper methods for general agents.

[`train`](../dopamine/discrete_domains/train.md) module: The entry point for
running a Dopamine agent.
[tab] third_party/dopamine/docs/api_docs/python/dopamine/jax/agents/rainbow/rainbow_agent/JaxRainbowAgent.md (markdown)
description: A compact implementation of a simplified Rainbow agent.

<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="dopamine.jax.agents.rainbow.rainbow_agent.JaxRainbowAgent" />
<meta itemprop="path" content="Stable" />
</div>

# dopamine.jax.agents.rainbow.rainbow_agent.JaxRainbowAgent

<!-- Insert buttons and diff -->

<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
  <a target="_blank" href="https://github.com/google/dopamine/tree/master/dopamine/jax/agents/rainbow/rainbow_agent.py#L206-L488">
    <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
    View source on GitHub
  </a>
</td>
</table>



A compact implementation of a simplified Rainbow agent.

Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md)

<!-- Placeholder for "Used in" -->
[tab] slurm/jobs/franz/berlin/atari/tokenizer/atari_breakout_tokenizer.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/tokenizer/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/tokenizer/%x_%j.log
#SBATCH --job-name=train_tokenizer_coinrun_reproduction_codebook_normal_init

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

tags="atari reproduction 10m_dataset tokenizer codebook_normal_init"

train_data_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/data/breakout/train"
val_data_dir="/fast/project/HFMI_SynergyUnit/jafar_ws/data/breakout/val"

CHECKPOINT_DIR="/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/atari/tokenizer/${job_name}_${slurm_job_id}"
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --wandb_id $SLURM_JOB_ID \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --image_height=64 \
    --image_width=64 \
    --init_lr=0 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name="${job_name}_${slurm_job_id}" \
    --tags $tags \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir &

child_pid=$!

wait $child_pid
[tab] slurm/jobs/franz/berlin/atari/legacy_cleanrl_data_generation/atari_breakout_data_gen.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/breakout
[content] slurm/jobs/franz/berlin/atari/data_generation/atari_breakout_no_sticky_actions.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/breakout
[content] slurm/jobs/franz/berlin/atari/data_generation/atari_pong_no_sticky_actions.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/breakout --gin_files=jasmine_data/atari/breakout_no_sticky_actions.gin
[tab] data/jasmine_data/atari/rainbow_no_sticky_actions.gin (plaintext)
# Hyperparameters follow Hessel et al. (2018)
import dopamine.jax.agents.rainbow.rainbow_agent
import dopamine.jax.replay_memory.replay_buffer
import dopamine.jax.replay_memory.samplers
import dopamine.discrete_domains.atari_lib
import dopamine.discrete_domains.run_experiment

JaxRainbowAgent.num_atoms = 51
JaxRainbowAgent.vmax = 10.
JaxRainbowAgent.gamma = 0.99
JaxRainbowAgent.update_horizon = 3
JaxRainbowAgent.min_replay_history = 20000  # agent steps
JaxRainbowAgent.update_period = 4
JaxRainbowAgent.target_update_period = 8000  # agent steps
JaxRainbowAgent.epsilon_train = 0.01
JaxRainbowAgent.epsilon_eval = 0.001
JaxRainbowAgent.epsilon_decay_period = 250000  # agent steps
JaxRainbowAgent.replay_scheme = 'prioritized'

# Note these parameters are different from C51's.
create_optimizer.learning_rate = 0.0000625
create_optimizer.eps = 0.00015

atari_lib.create_atari_environment.game_name = 'Breakout'
# No sticky actions unlike suggested by prior work (Machado et al., 2017) since we want the actions to be ground-truth
atari_lib.create_atari_environment.sticky_actions = False
create_runner.schedule = 'continuous_train'
create_agent.agent_name = 'jax_rainbow'
create_agent.debug_mode = True
Runner.num_iterations = 200
Runner.training_steps = 250000  # agent steps
Runner.evaluation_steps = 125000  # agent steps
Runner.max_steps_per_episode = 27000  # agent steps

ReplayBuffer.max_capacity = 1_000_000
ReplayBuffer.batch_size = 32
PrioritizedSamplingDistribution.max_capacity = 1_000_000
[tab] third_party/dopamine/docs/api_docs/python/dopamine/agents/rainbow/rainbow_agent/RainbowAgent.md (markdown)
description: A compact implementation of a simplified Rainbow agent.

<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="dopamine.agents.rainbow.rainbow_agent.RainbowAgent" />
<meta itemprop="path" content="Stable" />
</div>

# dopamine.agents.rainbow.rainbow_agent.RainbowAgent

<!-- Insert buttons and diff -->

<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
  <a target="_blank" href="https://github.com/google/dopamine/tree/master/dopamine/agents/rainbow/rainbow_agent.py">
    <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
    View source on GitHub
  </a>
</td>
</table>

A compact implementation of a simplified Rainbow agent.

Inherits From:
[`DQNAgent`](../../../../dopamine/agents/dqn/dqn_agent/DQNAgent.md)

<!-- Placeholder for "Used in" -->
[content] slurm/jobs/franz/berlin/atari/data_generation/atari_pong_no_sticky_actions.sh (shellscript)
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[tab] slurm/jobs/franz/berlin/atari/data_generation/breakout.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/alien.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/amidar.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/assault.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/asterix.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/bank_heist.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_breakout_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/breakout \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Breakout'
[content] slurm/jobs/franz/berlin/atari/data_generation/battle_zone.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_bank_heist_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/bank_heist \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='BankHeist'
[content] slurm/jobs/franz/berlin/atari/data_generation/boxing.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_bank_heist_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/bank_heist \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='BankHeist'
[content] slurm/jobs/franz/berlin/atari/data_generation/chopper_command.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_bank_heist_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/bank_heist \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='BankHeist'
[content] slurm/jobs/franz/berlin/atari/data_generation/crazy_climber.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_bank_heist_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/bank_heist \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='BankHeist'
[content] slurm/jobs/franz/berlin/atari/data_generation/demon_attack.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_bank_heist_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/bank_heist \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='BankHeist'
[tab] slurm/dev/alfred/berlin/test_exposure_bias/batch_scaling/spawner.sh (shellscript)
# batch_sizes=(128 248 256 512)

# from 36 to 128 in steps of 16
batch_sizes=(48 64 80 96 112 )

# steps of 16 
# batch_sizes=(272 320 368 384 400 )



# batch_sizes=(512 750 1024)

for batch_size in "${batch_sizes[@]}"; do
    BATCH_SIZE=${batch_size} \
    sbatch \
    --job-name="dynamics_coinrun_exposure_bias_batch_scaling_${batch_size}" \
    slurm/dev/alfred/berlin/test_exposure_bias/batch_scaling/dynamics_batch_scaling.sbatch
done
[tab] slurm/jobs/franz/berlin/atari/data_generation/pong.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=64
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/atari/%x_%j.log
#SBATCH --job-name=generate_atari_pong_no_sticky_actions_data

cd data/
uv run python jasmine_data/atari/generate_atari_dataset.py \
 --output_dir=/fast/project/HFMI_SynergyUnit/jafar_ws/data/atari/pong \
 --gin_files=jasmine_data/atari/rainbow_no_sticky_actions.gin \
 --gin_bindings=atari_lib.create_atari_environment.game_name='Pong'