sgoodfriend/dqn-LunarLander-v2

DQN Agent playing LunarLander-v2

This is a trained model of a DQN agent playing LunarLander-v2 using the /sgoodfriend/rl-algo-impls repo.

All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.

Training Results

This model was trained from 3 trainings of DQN agents using different initial seeds. These agents were trained by checking out 983cb75. The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).

algo	env	seed	reward_mean	reward_std	eval_episodes	best	wandb_url
dqn	LunarLander-v2	1	280.412	22.8894	10	*	wandb
dqn	LunarLander-v2	2	253.237	41.1565	10		wandb
dqn	LunarLander-v2	3	251.723	35.4412	10		wandb

Prerequisites: Weights & Biases (WandB)

Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB.

Before doing anything below, you'll need to create a wandb account and run wandb login.

Usage

/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls

Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: 983cb75.

# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/pc5m5bh0

Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the colab_enjoy.ipynb notebook.

Training

If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: 983cb75. While training is deterministic, different hardware will give different results.

python train.py --algo dqn --env LunarLander-v2 --seed 1

Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the colab_train.ipynb notebook.

Benchmarking (with Lambda Labs instance)

This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal:

git clone git@github.com:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]

Alternative: Google Colab Pro+

As an alternative, colab_benchmark.ipynb, can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit.

Hyperparameters

This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data:

additional_keys_to_log: []
algo: dqn
algo_hyperparams:
  batch_size: 256
  buffer_size: 100000
  exploration_final_eps: 0.1
  exploration_fraction: 0.12
  gamma: 0.99
  gradient_steps: -1
  learning_rate: 0.0001
  learning_starts: 10000
  max_grad_norm: 0.5
  target_update_interval: 250
  train_freq: 8
device: auto
env: LunarLander-v2
env_hyperparams:
  rolling_length: 50
env_id: null
eval_hyperparams:
  step_freq: 25000
microrts_reward_decay_callback: false
n_timesteps: 500000
policy_hyperparams:
  hidden_sizes:
  - 256
  - 256
seed: 1
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9