ray/rllib/tuned_examples/pong-dqn.yaml
2020-02-20 08:39:16 -08:00

22 lines
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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
exploration_config:
epsilon_timesteps: 200000
final_epsilon: .01
model:
grayscale: True
zero_mean: False
dim: 42