[Algorithm] IMPALA and VTrace module

.github/unittest/linux_examples/scripts/run_test.sh

@@ -52,6 +52,12 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/decision_trans
 # ==================================================================================== #
 # ================================ Gymnasium ========================================= #
 
+python .github/unittest/helpers/coverage_run_parallel.py examples/impala/impala_single_node.py \
+  collector.total_frames=80 \
+  collector.frames_per_batch=20 \
+  collector.num_workers=1 \
+  logger.backend= \
+  logger.test_interval=10
 python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo_mujoco.py \
   env.env_name=HalfCheetah-v4 \
   collector.total_frames=40 \

examples/distributed/collectors/multi_nodes/ray_train.py

@@ -117,7 +117,7 @@
         "object_store_memory": 1024**3,
     }
     collector = RayCollector(
-        env_makers=[env] * num_collectors,
+        create_env_fn=[env] * num_collectors,
         policy=policy_module,
         collector_class=SyncDataCollector,
         collector_kwargs={

examples/impala/README.md

@@ -0,0 +1,33 @@
+## Reproducing Importance Weighted Actor-Learner Architecture (IMPALA) Algorithm Results
+
+This repository contains scripts that enable training agents using the IMPALA Algorithm on MuJoCo and Atari environments. We follow the original paper [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) by Espeholt et al. 2018.
+
+## Examples Structure
+
+Please note that we provide 2 examples, one for single node training and one for distributed training. Both examples rely on the same utils file, but besides that are independent. Each example contains the following files:
+
+1. **Main Script:** The definition of algorithm components and the training loop can be found in the main script  (e.g. impala_single_node_ray.py).
+
+2. **Utils File:** A utility file is provided to contain various helper functions, generally to create the environment and the models (e.g. utils.py).
+
+3. **Configuration File:** This file includes default hyperparameters specified in the original paper. For the multi-node case, the file also includes the configuration file of the Ray cluster. Users can modify these hyperparameters to customize their experiments  (e.g. config_single_node.yaml).
+
+
+## Running the Examples
+
+You can execute the single node IMPALA algorithm on Atari environments by running the following command:
+
+```bash
+python impala_single_node.py
+```
+
+You can execute the multi-node IMPALA algorithm on Atari environments by running the following command:
+
+```bash
+python impala_single_node_ray.py
+``` 
+or 
+
+```bash
+python impala_single_node_submitit.py
+``` 

examples/impala/config_multi_node_ray.yaml

@@ -0,0 +1,65 @@
+# Environment
+env:
+  env_name: PongNoFrameskip-v4
+
+# Ray init kwargs - https://docs.ray.io/en/latest/ray-core/api/doc/ray.init.html
+ray_init_config:
+  address: null
+  num_cpus: null
+  num_gpus: null
+  resources: null
+  object_store_memory: null
+  local_mode: False
+  ignore_reinit_error: False
+  include_dashboard: null
+  dashboard_host: 127.0.0.1
+  dashboard_port: null
+  job_config: null
+  configure_logging: True
+  logging_level: info
+  logging_format: null
+  log_to_driver: True
+  namespace: null
+  runtime_env: null
+  storage: null
+
+# Device for the forward and backward passes
+local_device: "cuda:0"
+
+# Resources assigned to each IMPALA rollout collection worker
+remote_worker_resources:
+  num_cpus: 1
+  num_gpus: 0.25
+  memory: 1073741824 # 1*1024**3 - 1GB
+
+# collector
+collector:
+  frames_per_batch: 80
+  total_frames: 200_000_000
+  num_workers: 12
+
+# logger
+logger:
+  backend: wandb
+  exp_name: Atari_IMPALA
+  test_interval: 200_000_000
+  num_test_episodes: 3
+
+# Optim
+optim:
+  lr: 0.0006
+  eps: 1e-8
+  weight_decay: 0.0
+  momentum: 0.0
+  alpha: 0.99
+  max_grad_norm: 40.0
+  anneal_lr: True
+
+# loss
+loss:
+  gamma: 0.99
+  batch_size: 32
+  sgd_updates: 1
+  critic_coef: 0.5
+  entropy_coef: 0.01
+  loss_critic_type: l2

examples/impala/config_multi_node_submitit.yaml

@@ -0,0 +1,46 @@
+# Environment
+env:
+  env_name: PongNoFrameskip-v4
+
+# Device for the forward and backward passes
+local_device: "cuda:0"
+
+# SLURM config
+slurm_config:
+  timeout_min: 10
+  slurm_partition: train
+  slurm_cpus_per_task: 1
+  slurm_gpus_per_node: 1
+
+# collector
+collector:
+  backend: gloo
+  frames_per_batch: 80
+  total_frames: 200_000_000
+  num_workers: 1
+
+# logger
+logger:
+  backend: wandb
+  exp_name: Atari_IMPALA
+  test_interval: 200_000_000
+  num_test_episodes: 3
+
+# Optim
+optim:
+  lr: 0.0006
+  eps: 1e-8
+  weight_decay: 0.0
+  momentum: 0.0
+  alpha: 0.99
+  max_grad_norm: 40.0
+  anneal_lr: True
+
+# loss
+loss:
+  gamma: 0.99
+  batch_size: 32
+  sgd_updates: 1
+  critic_coef: 0.5
+  entropy_coef: 0.01
+  loss_critic_type: l2

examples/impala/config_single_node.yaml

@@ -0,0 +1,38 @@
+# Environment
+env:
+  env_name: PongNoFrameskip-v4
+
+# Device for the forward and backward passes
+device: "cuda:0"
+
+# collector
+collector:
+  frames_per_batch: 80
+  total_frames: 200_000_000
+  num_workers: 12
+
+# logger
+logger:
+  backend: wandb
+  exp_name: Atari_IMPALA
+  test_interval: 200_000_000
+  num_test_episodes: 3
+
+# Optim
+optim:
+  lr: 0.0006
+  eps: 1e-8
+  weight_decay: 0.0
+  momentum: 0.0
+  alpha: 0.99
+  max_grad_norm: 40.0
+  anneal_lr: True
+
+# loss
+loss:
+  gamma: 0.99
+  batch_size: 32
+  sgd_updates: 1
+  critic_coef: 0.5
+  entropy_coef: 0.01
+  loss_critic_type: l2