comoer / hrddat

AI3614 GTML final project

Heuristic Reward Driven Athlete Trainer

Use parallel framework and curiosity driven reward to train a running athlete

Our method pipeline

Our group

Yu Zheng (519030910354) @COMoER for the parallel framework.

Jiahang Cao (519030910347)@TopsailCBD for the astar costmap and trace potential reward.

Qingquan Bao (519030910402)@QingquanBao for the curiosity inner reawrd and self-play settings.

The experiment report is equally contributed by the three authors.

Our method demo

Curiosity Athlete(agent0,ours) compete with random(agent1)

Curiosity Athlete(agent0,ours) compete with jidi baseline agent trained in map4 (agent1)

Usage

• install

pip install -r requirement.txt
# pre generate costmap for astar reward
python astarmap.py

• training

# training baseline with map1
python rl_trainer/main.py --device cuda --map 1
# training baseline with shuffle map
python rl_trainer/main.py --device cuda --shuffle_map
# training HRDDAT best model (we trained on the 64 cpu server)
python rl_trainer/main_parallel_curiosity.py --device cuda --reward_norm --data_norm --advt_norm --curiosity --num_rollouts 36 --max_length 500 --shuffle_map --ext_ratio 0.1 --curiosity_ratio 0.9

• evaluation

You should change the rundir and episode to evaluate in the submit_agent/{YOUR_AGENT_NAME}/submission.py

####################
run = 1 # choose which run package to evaluate
episode = 296 # choose which episode check point to evaluate
####################

To evaluate locally, add --render to play the UI meanwhile

# evaluate the model with random opponent
python evaluation_local.py --my_ai ppo_curiosity --opponent random --shuffle_map
# evaluate the model with jidi rl opponent
python evaluation_local.py --my_ai ppo_curiosity  --opponent rl --shuffle_map
# evaluate the baseline model(trained by main.py) with random opponent
python evaluation_local.py --my_ai ppo --opponent random --shuffle_map

Acknowledgement

Modified from https://github.com/sjtu-marl/Competition_Olympics-Running.

• add evaluate_local script to accept submission.py evaluation
• HRDDAT model train script added (main_parallel_curiosity.py)

The profile function(rl_trainer/algo/prof.py) is from https://github.com/facebookresearch/torchbeast

The parallel framework refers to https://github.com/marlbenchmark/on-policy