Brain Agent

Brain Agent is a distributed agent learning system for large-scale and multi-task reinforcement learning, developed by Kakao Brain. Brain Agent is based on the V-trace actor-critic framework IMPALA and modifies Sample Factory to utilize multiple GPUs and CPUs for a much higher throughput rate during training.

Features

1. First publicly available implementations of reproducing SOTA results on DMLAB30.
2. Scalable & massive throughput.
   BrainAgent can produce and train 20B frames/week, or 34K fps, with 16 V100 GPUs, by scaling up high throughput single node system Sample Factory.
3. Based on following algorithms and architectures.
   • TransformerXL-I core and ResNet encoder
   • V-trace for update algorithm
   • IMPALA for system framework
   • PopArt for multitask handling
4. For self-supervised representation learning, we include 2 additional features.
   • ResNet-based decoder to reconstruct the original input image (trxl_recon)
   • Additional autoregressive transformer to predict the images of future steps from the current state embedding and future action sequence (trxl_future_pred)
5. Provide codes for both training and evaluation, along with SOTA model checkpoint with 28M params.

How to Install

• Python 3.7
• Pytorch 1.9.0
• CUDA 11.1
• Install DMLab envrionment - DMLab Github
• pip install -r requirements.txt

Description of Codes

• dist_launch.py -> distributed training launcher
• eval.py -> entry point for evaluation
• train.py -> entry point for training
• brain_agent
  • core
    • agents
      • dmlab_multitask_agent.py
    • algos
      • aux_future_predict.py -> Computes auxiliary loss by predicting future state transitions with autoregressive transformer. Used only for (trxl_future_pred).
      • popart.py
      • vtrace.py
    • actor_worker.py
    • learner_worker.py
    • policy_worker.py
    • shared_buffer.py -> Defines SharedBuffer class for zero-copy communication between workers.
  • envs
    • dmlab
  • utils
    • ...
• configs
  • ... -> Hyperparam configs for each of training/evaluation.

How to Run

Training

• 1 node x 1 GPU

     python train.py cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR

• 1 node x 4 GPUs = 4 GPUs

     python -m dist_launch --nnodes=1 --node_rank=0 --nproc_per_node=4 -m train \ 
       cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR

• 4 nodes x 4 GPUs each = 16 GPUs

     sleep 120; python -m dist_launch --nnodes=4 --node_rank=0 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m train \ 
       cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR 
     sleep 120; python -m dist_launch --nnodes=4 --node_rank=1 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m train \ 
       cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR
     sleep 120; python -m dist_launch --nnodes=4 --node_rank=2 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m train \ 
       cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR
     sleep 120; python -m dist_launch --nnodes=4 --node_rank=3 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m train \ 
       cfg=configs/trxl_recon_train.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR

Evaluation

python eval.py cfg=configs/trxl_recon_eval.yaml train_dir=$TRAIN_DIR experiment=$EXPERIMENT_DIR test.checkpoint=$CHECKPOINT_FILE_PATH 

Setting Hyperparameters

• All the default hyperparameters are defined at configs/default.yaml
• Other config files override on configs/default.yaml.
• You can use pre-defined hyperparameters for our experiments with configs/trxl_recon_train.yaml or configs/trxl_future_pred.yaml.

Results for DMLAB30

• Settings

  • 3 runs with different seeds
  • 100 episodes per each run
  • HNS : Human Normalised Score

• Results

  Model	Mean HNS	Median HNS	Mean Capped HNS
  MERLIN	115.2	-	89.4
  GTrXL	117.6	-	89.1
  CoBERL	115.47	110.86	-
  R2D2+	-	99.5	85.7
  LASER	-	97.2	81.7
  PBL	104.16	-	81.5
  PopArt-IMPALA	-	-	72.8
  IMPALA	-	-	58.4
  Ours (lstm_baseline, 20B ckpt)	103.03 ± 0.37	92.04 ± 0.73	81.35 ± 0.25
  Ours (trxl_baseline, 20B ckpt)	111.95 ± 1.00	105.43 ± 2.61	85.57 ± 0.20
  Ours (trxl_recon, 20B ckpt)	123.60 ± 0.84	108.63 ± 1.20	91.25 ± 0.41
  Ours (trxl_future_pred, 20B ckpt)	128.00 ± 0.43	108.80 0.99	90.53 ± 0.26

Results for all 30 tasks

Level	lstm_baseline	trxl_baseline	trxl_recon	trxl_future_pred
rooms_collect_good_objects_(train / test)	94.22 ± 0.84 / 95.13 ± 0.61	97.85 ± 0.31 / 95.20 ± 1.26	97.58 ± 0.20 / 89.39 ± 1.42	98.19 ± 0.18 / 98.52 ± 0.95
rooms_exploit_deferred_effects_(train / test)	37.84 ± 2.23 / 4.36 ± 1.84	38.40 ± 3.82 / 1.73 ± 0.63	38.86 ± 3.48 / 4.04 ± 0.89	40.93 ± 3.12 / 2.26 ± 0.71
rooms_select_nonmatching_object	50.13 ± 2.95	98.78 ± 1.38	99.52 ± 0.97	113.20 ± 1.14
rooms_watermaze	45.09 ± 4.70	36.92 ± 6.90	111.20 ± 2.29	55.82 ± 0.74
rooms_keys_doors_puzzle	51.75 ± 8.90	55.86 ± 4.25	61.24 ± 9.09	64.95 ± 8.43
language_select_described_object	150.57 ± 0.58	154.90 ± 0.22	155.35 ± 0.17	158.23 ± 0.90
language_select_located_object	225.97 ± 1.93	244.46 ± 1.56	252.04 ± 0.31	261.20 ± 1.15
language_execute_random_task	126.49 ± 2.35	139.63 ± 1.23	145.21 ± 0.36	150.20 ± 1.35
language_answer_quantitative_question	153.92 ± 2.35	162.99 ± 2.42	163.72 ± 1.36	166.07 ± 1.72
lasertag_one_opponent_small	234.90 ± 6.19	243.52 ± 3.96	249.99 ± 6.64	279.54 ± 4.14
lasertag_three_opponents_small	235.61 ± 1.92	242.61 ± 3.75	246.68 ± 5.99	264.20 ± 3.76
lasertag_one_opponent_large	74.88 ± 5.06	83.51 ± 1.31	82.55 ± 2.15	94.86 ± 3.64
lasertag_three_opponents_large	84.78 ± 2.42	92.04 ± 2.17	96.54 ± 0.67	105.83 ± 0.47
natlab_fixed_large_map	98.10 ± 1.77	110.74 ± 1.34	120.53 ± 1.79	118.17 ± 1.79
natlab_varying_map_regrowth	108.54 ± 1.20	107.16 ± 2.68	108.14 ± 1.25	104.83 ± 1.26
natlab_varying_map_randomized	85.33 ± 6.52	86.33 ± 7.30	85.53 ± 6.69	77.74 ± 0.84
skymaze_irreversible_path_hard	55.29 ± 9.08	60.63 ± 4.73	61.63 ± 2.52	66.30 ± 5.69
skymaze_irreversible_path_varied	77.02 ± 3.57	77.41 ± 0.67	81.31 ± 2.34	79.36 ± 7.95
psychlab_arbitrary_visuomotor_mapping	52.17 ± 2.06	51.46 ± 0.45	101.82 ± 0.19	101.80 ± 0.00
psychlab_continuous_recognition	52.57 ± 0.46	52.41 ± 0.92	102.46 ± 0.32	102.30 ± 0.00
psychlab_sequential_comparison	76.82 ± 0.45	75.48 ± 1.16	75.74 ± 0.58	76.13 ± 0.77
psychlab_visual_search	101.54 ± 0.10	101.58 ± 0.04	101.91 ± 0.00	101.90 ± 0.00
explore_object_locations_small	118.89 ± 0.93	121.47 ± 0.26	123.54 ± 2.61	126.67 ± 2.08
explore_object_locations_large	111.46 ± 2.91	120.70 ± 2.12	115.43 ± 1.64	129.83 ± 2.41
explore_obstructed_goals_small	136.92 ± 6.02	148.05 ± 1.96	166.75 ± 3.63	174.30 ± 3.72
explore_obstructed_goals_large	92.36 ± 5.81	106.73 ± 7.86	153.44 ± 3.20	176.43 ± 1.50
explore_goal_locations_small	143.21 ± 8.21	154.87 ± 4.41	177.16 ± 0.37	193.00 ± 3.75
explore_goal_locations_large	98.50 ± 9.61	117.33 ± 6.75	160.39 ± 3.32	178.13 ± 7.15
explore_object_rewards_few	76.29 ± 1.52	108.64 ± 0.89	109.58 ± 3.53	110.07 ± 1.42
explore_object_rewards_many	72.33 ± 0.87	105.33 ± 1.52	105.15 ± 0.75	107.23 ± 1.59

• Learning curves
  

Distributed RL System Overview

Notes

• Acknowledgement

  • Sample Factory for optimized single node training, Transformer-XL for neural net core architecture.

• License

  • This repository is released under the MIT license, included here.
  • This repository includes some codes from sample-factory (MIT license) and transformer-xl (Apache 2.0 License).

• Contact

  • Agent learning team, Kakao Brain.
  • If you have any question or feedback regarding this repository, please email to [email protected]

Citation

@misc{kakaobrain2022brain_agent, title = {Brain Agent}, 
author = {Donghoon Lee, Taehwan Kwon, Seungeun Rho, Daniel Wontae Nam, Jongmin Kim, Daejin Jo, and Sungwoong Kim}, 
year = {2022}, howpublished = {\url{https://github.com/kakaobrain/brain_agent}} }