ray/rllib/tuned_examples/pong-dqn.yaml

22 lines
591 B
YAML

# You can expect ~20 reward within 1.1m timesteps / 2.1 hours on a K80 GPU
pong-deterministic-dqn:
env: PongDeterministic-v4
run: DQN
stop:
episode_reward_mean: 20
time_total_s: 7200
config:
num_gpus: 1
gamma: 0.99
lr: .0001
learning_starts: 10000
buffer_size: 50000
sample_batch_size: 4
train_batch_size: 32
schedule_max_timesteps: 2000000
exploration_final_eps: .01
exploration_fraction: .1
model:
grayscale: True
zero_mean: False
dim: 42