a2c-LunarLander-v2 / README.md
araffin's picture
Update README.md
f98e0ce
|
raw
history blame
1.88 kB
metadata
library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: A2C
    results:
      - metrics:
          - type: mean_reward
            value: 111.57 +/- 98.19
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2

A2C Agent playing LunarLander-v2

This is a trained model of a A2C agent playing LunarLander-v2 using the stable-baselines3 library.

Training code (with Stable-baselines3)

from stable_baselines3 import A2C
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.callbacks import EvalCallback

# Create the environment
env_id = "LunarLander-v2"
n_envs = 8
env = make_vec_env(env_id, n_envs=n_envs)

# Create the evaluation envs
eval_envs = make_vec_env(env_id, n_envs=5)

# Adjust evaluation interval depending on the number of envs
eval_freq = int(1e5)
eval_freq = max(eval_freq // n_envs, 1)

# Create evaluation callback to save best model
# and monitor agent performance
eval_callback = EvalCallback(
    eval_envs,
    best_model_save_path="./logs/",
    eval_freq=eval_freq,
    n_eval_episodes=10,
)


# Instantiate the agent
# Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
linear_schedule = lambda progress_remaining: progress_remaining * 0.00083
model = A2C(
    "MlpPolicy",
    env,
    n_steps=5,
    gamma=0.995,
    learning_rate=linear_schedule,
    ent_coef=0.00001,
    verbose=1,
)

# Train the agent (you can kill it before using ctrl+c)
try:
    model.learn(total_timesteps=int(5e5), callback=eval_callback)
except KeyboardInterrupt:
    pass

# Load best model
model = A2C.load("logs/best_model.zip")