ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms.
For research on RTS games, ELF comes with an fast RTS engine, and three concrete environments: MiniRTS, Capture the Flag and Tower Defense. MiniRTS has all the key dynamics of a real-time strategy game, including gathering resources, building facilities and troops, scouting the unknown territories outside the perceivable regions, and defend/attack the enemy. User can access its internal representation and can freely change the game setting.
ELF has the following characteristics:
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End-to-End: ELF offers an end-to-end solution to game research. It provides miniature real-time strategy game environments, concurrent simulation, intuitive APIs, web-based visualzation, and also comes with a reinforcement learning backend empowered by Pytorch with minimal resource requirement.
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Extensive: Any game with C/C++ interface can be plugged into this framework by writing a simple wrapper. As an example, we already incorporate Atari games into our framework and show that the simulation speed per core is comparable with single-core version, and is thus much faster than implementation using either multiprocessing or Python multithreading. In the future, we plan to incorporate more environments, e.g., DarkForest Go engine.
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Lightweight: ELF runs very fast with minimal overhead. ELF with a simple game (MiniRTS) built on RTS engine runs 40K frame per second per core on a MacBook Pro. Training a model from scratch to play MiniRTS takes a day on 6 CPU + 1 GPU.
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Flexible: Pairing between environments and actors is very flexible, e.g., one environment with one agent (e.g., Vanilla A3C), one environment with multiple agents (e.g., Self-play/MCTS), or multiple environment with one actor (e.g., BatchA3C, GA3C). Also, any game built on top of the RTS engine offers full access to its internal representation and dynamics. Besides efficient simulators, we also provide a lightweight yet powerful Reinforcement Learning framework. This framework can host most existing RL algorithms. In this open source release, we have provided state-of-the-art actor-critic algorithms, written in PyTorch.
Check here for detailed documentation. You can also compile your version in ./doc using sphinx.
ELF is very easy to use. The initialization looks like the following:
# We run 1024 games concurrently.
num_games = 1024
# Wait for a batch of 256 games.
batchsize = 256
# The return states contain key 's', 'r' and 'terminal'
# The reply contains key 'a' to be filled from the Python side.
# The definitions of the keys are in the wrapper of the game.
input_spec = dict(s='', r='', terminal='')
reply_spec = dict(a='')
context = Init(num_games, batchsize, input_spec, reply_spec)The main loop is also very simple:
# Start all game threads and enter main loop.
context.Start()
while True:
# Wait for a batch of game states to be ready
# These games will be blocked, waiting for replies.
batch = context.Wait()
# Apply a model to the game state. The output has key 'pi'
# You can do whatever you want here. E.g., applying your favorite RL algorithms.
output = model(batch)
# Sample from the output to get the actions of this batch.
reply['a'][:] = SampleFromDistribution(output)
# Resume games.
context.Steps()
# Stop all game threads.
context.Stop() C++ compiler with C++11 support (e.g., gcc >= 4.9) is required. Python3.x is required. The following libraries are required:
tbb
tqdm
To train a model for MiniRTS, please first compile ./rts/game_MC (See the instruction in ./rts/game_MC and ./rts). Note that a compilation of ./rts/backend is not necessary for training, unless you want to see visualization.
Then please run the following commands in the current directory:
game=./rts/game_MC/game model=actor_critic model_file=./rts/game_MC/model \
python3 run.py
--num_games 1024 --batchsize 128 # Set number of games to be 1024 and batchsize to be 128.
--freq_update 50 # Update behavior policy after 50 updates of the model.
--fs_opponent 20 # How often your opponent makes a decision (every 20 ticks)
--latest_start 500 --latest_start_decay 0.99 # Use rule-based AI for the first 500 ticks, then trained AI takes over. latest_start decays with rate latest_start_decay.
--opponent_type AI_SIMPLE # Use AI_SIMPLE as rule-based AI
--tqdm # Show progress bar.
--gpu 0 # Use first gpu.
--T 20 # 20 step actor-criticNote that long horizon (e.g., --T 20) could make the training much faster and (at the same time) stable. With long horizon, you should be able to train it to 70% winrate within 12 hours with 16CPU and 1GPU. You can control the number of CPUs used in the training using taskset -c.
Here is one pretrained model with T = 16. Here is one game replay.
The following is a sample output during training:
$ game=./rts/game_MC/game model=actor_critic model_file=./rts/game_MC/model taskset -c 0-9 python3 run.py --batchsize 128 --freq_update 50 --fs_opponent 20 --latest_start 500 --latest_start_decay 0.99 --num_games 1024 --opponent_type AI_SIMPLE --tqdm
Namespace(T=6, actor_only=False, ai_type='AI_NN', batchsize=128, discount=0.99, entropy_ratio=0.01, epsilon=0.0, eval=False, freq_update=50, fs_ai=50, fs_opponent=20, game_multi=None, gpu=None, grad_clip_norm=None, greedy=False, handicap_level=0, latest_start=500, latest_start_decay=0.99, load=None, max_tick=30000, mcts_threads=64, min_prob=1e-06, num_episode=10000, num_games=1024, num_minibatch=5000, opponent_type='AI_SIMPLE', ratio_change=0, record_dir='./record', sample_node='pi', sample_policy='epsilon-greedy', save_dir=None, save_prefix='save', seed=0, simple_ratio=-1, tqdm=True, verbose_collector=False, verbose_comm=False, wait_per_group=False)
Version: bf1304010f9609b2114a1adff4aa2eb338695b9d_staged
Num Actions: 9
Num unittype: 6
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5000/5000 [01:35<00:00, 52.37it/s]
[2017-07-12 09:04:13.212017][128] Iter[0]:
Train count: 820/5000, actor count: 4180/5000
Save to ./
Filename = ./save-820.bin
Command arguments run.py --batchsize 128 --freq_update 50 --fs_opponent 20 --latest_start 500 --latest_start_decay 0.99 --num_games 1024 --opponent_type AI_SIMPLE --tqdm
0:acc_reward[4100]: avg: -0.34079, min: -0.58232[1580], max: 0.25949[185]
0:cost[4100]: avg: 2.15912, min: 1.97886[2140], max: 2.31487[1173]
0:entropy_err[4100]: avg: -2.13493, min: -2.17945[438], max: -2.04809[1467]
0:init_reward[820]: avg: -0.34093, min: -0.56980[315], max: 0.26211[37]
0:policy_err[4100]: avg: 2.16714, min: 1.98384[1520], max: 2.31068[1176]
0:predict_reward[4100]: avg: -0.33676, min: -1.36083[1588], max: 0.39551[195]
0:reward[4100]: avg: -0.01153, min: -0.13281[1109], max: 0.04688[124]
0:rms_advantage[4100]: avg: 0.15646, min: 0.02189[800], max: 0.79827[564]
0:value_err[4100]: avg: 0.01333, min: 0.00024[800], max: 0.06569[1549]
86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 4287/5000 [01:23<00:15, 46.97it/s]
To evaluate a model for MiniRTS, try the following command:
eval_only=1 game=./rts/game_MC/game model=actor_critic model_file=./rts/game_MC/model \
python3 run.py
--load [your model]
--batchsize 128
--fs_opponent 20
--latest_start 500
--latest_start_decay 0.99
--num_games 1024
--opponent_type AI_SIMPLE
--stats winrate
--num_eval 10000
--tqdm
--eval_gpu 0 # Use GPU 0 as the evaluation gpu.
--additional_labels id # Tell the game environment to output additional dict entries.Here is an example output (it takes 1 min 40 seconds to evaluate 10k games with 12 CPUs):
$ rts eval_only=1 game=./rts/game_MC/game model_file=./rts/game_MC/model model=actor_critic taskset -c 12-23 python3 run.py --num_games 128 --batchsize 32 --tqdm --load model-minirts-212808.bin --eval_gpu 4 --fs_opponent 50 --latest_start 0 --opponent_type AI_SIMPLE --stats winrate --num_eval 10000 --additional_labels id
Namespace(T=6, actor_only=False, additional_labels='id', ai_type='AI_NN', batchsize=32, discount=0.99, entropy_ratio=0.01, epsilon=0.0, eval=False, eval_freq=10, eval_gpu=4, freq_update=1, fs_ai=50, fs_opponent=50, game_multi=None, gpu=None, grad_clip_norm=None, greedy=False, handicap_level=0, latest_start=0, latest_start_decay=0.7, load='./save-212808.bin', max_tick=30000, mcts_threads=64, min_prob=1e-06, num_episode=10000, num_eval=10000, num_games=128, num_minibatch=5000, opponent_type='AI_SIMPLE', ratio_change=0, record_dir='./record', sample_node='pi', sample_policy='epsilon-greedy', save_dir=None, save_prefix='save', seed=0, simple_ratio=-1, stats='winrate', tqdm=True, verbose_collector=False, verbose_comm=False, wait_per_group=False)
Version: dc895b8ea7df8ef7f98a1a031c3224ce878d52f0_
Num Actions: 9
Num unittype: 6
Load from ./save-212808.bin
Version: dc895b8ea7df8ef7f98a1a031c3224ce878d52f0_
Num Actions: 9
Num unittype: 6
100%|████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [01:40<00:00, 99.94it/s]
str_acc_win_rate: Accumulated win rate: 0.735 [7295/2628/9923]
best_win_rate: 0.7351607376801297
new_record: True
count: 0
str_win_rate: [0] Win rate: 0.735 [7295/2628/9923], Best win rate: 0.735 [0]
Stop all game threads ...
When you use ELF, please reference the associated arXiv paper.