def main(unused_arg):
env = catch.Catch(seed=FLAGS.seed)
epsilon_cfg = dict(init_value=FLAGS.epsilon_begin,
end_value=FLAGS.epsilon_end,
transition_steps=FLAGS.epsilon_steps,
power=1.)
agent = DQN(
observation_spec=env.observation_spec(),
action_spec=env.action_spec(),
epsilon_cfg=epsilon_cfg,
target_period=FLAGS.target_period,
learning_rate=FLAGS.learning_rate,
)
accumulator = ReplayBuffer(FLAGS.replay_capacity)
experiment.run_loop(
agent=agent,
environment=env,
accumulator=accumulator,
seed=FLAGS.seed,
batch_size=FLAGS.batch_size,
train_episodes=FLAGS.train_episodes,
evaluate_every=FLAGS.evaluate_every,
eval_episodes=FLAGS.eval_episodes,
)
def main(unused_arg):
env = catch.Catch(seed=FLAGS.seed)
agent = OnlineQ(env.observation_spec(), env.action_spec(),
FLAGS.learning_rate, FLAGS.epsilon)
accumulator = TransitionAccumulator()
experiment.run_loop(agent, env, accumulator, FLAGS.seed, 1,
FLAGS.train_episodes, FLAGS.evaluate_every,
FLAGS.eval_episodes)
def main(unused_arg):
env = catch.Catch(seed=FLAGS.seed)
agent = OnlineQLambda(observation_spec=env.observation_spec(),
action_spec=env.action_spec(),
num_hidden_units=FLAGS.num_hidden_units,
epsilon=FLAGS.epsilon,
lambda_=FLAGS.lambda_,
learning_rate=FLAGS.learning_rate)
accumulator = SequenceAccumulator(length=FLAGS.sequence_length)
experiment.run_loop(
agent=agent,
environment=env,
accumulator=accumulator,
seed=FLAGS.seed,
batch_size=1,
train_episodes=FLAGS.train_episodes,
evaluate_every=FLAGS.evaluate_every,
eval_episodes=FLAGS.eval_episodes,
)
def main(unused_arg):
env = catch.Catch(seed=FLAGS.seed)
env = wrappers.RewardScale(env, reward_scale=FLAGS.reward_scale)
agent = PopArtAgent(
observation_spec=env.observation_spec(),
action_spec=env.action_spec(),
num_hidden_units=FLAGS.num_hidden_units,
epsilon=FLAGS.epsilon,
learning_rate=FLAGS.learning_rate,
pop_art_step_size=FLAGS.pop_art_step_size,
)
accumulator = TransitionAccumulator()
experiment.run_loop(
agent=agent,
environment=env,
accumulator=accumulator,
seed=FLAGS.seed,
batch_size=1,
train_episodes=FLAGS.train_episodes,
evaluate_every=FLAGS.evaluate_every,
eval_episodes=FLAGS.eval_episodes,
)
