def train(config, checkpoint_dir=None):
trainer = DQNTrainer(config=config, env='BomberMan-v0')
# trainer.restore('C:\\Users\\Florian\\ray_results\\PPO_BomberMan-v0_2021-03-16_09-20-44984tj3ip\\checkpoint_002770\\checkpoint-2770')
iter = 0
# def update_phase(ev):
# ev.foreach_env(lambda e: e.set_phase(phase))
while True:
iter += 1
result = trainer.train()
if iter % 250 == 0:
if not os.path.exists(f'./model-{iter}'):
trainer.get_policy('policy_01').export_model(
f'./model-{iter}')
else:
print("model already saved")
def test_policy_save_restore(self):
config = DEFAULT_CONFIG.copy()
for _ in framework_iterator(config):
trainer = DQNTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
state1 = policy.get_state()
trainer.train()
state2 = policy.get_state()
check(state1["_exploration_state"]["last_timestep"],
state2["_exploration_state"]["last_timestep"],
false=True)
check(state1["global_timestep"],
state2["global_timestep"],
false=True)
# Reset policy to its original state and compare.
policy.set_state(state1)
state3 = policy.get_state()
# Make sure everything is the same.
check(state1, state3)
# num_envs=NUM_ENVS)
# config = {"env": "warehouse_env",
# "framework": "torch",
# "num_gpus": 0.1,
# "num_gpus_per_worker": 0.1,
# 'num_envs_per_worker': 6,
# "evaluation_interval": 5, }
with open(params_path, "rb") as f:
config = cloudpickle.load(f)
config["explore"] = False
config['num_envs_per_worker'] = 1
print("Trained on map: \n", config["env_config"]["maps"])
config["env_config"]["maps"] = MAP_WITH_EXCEPTION
trainer = DQNTrainer(config=config)
trainer.restore(path.format(checkpoint, checkpoint))
policy = trainer.get_policy()
trainer._evaluate()
samples = (trainer.evaluation_workers.local_worker().sample()
for _ in range(NUM_EPISODES))
rows = map(lambda x: np.concatenate([
x["unroll_id"][:, None],
np.arange(0, x.count)[:,None],
x["obs"],
x["actions"][:, None],
x["q_values"],
x["rewards"][:, None],
x["dones"][:, None],
x["new_obs"],
process_info(x["infos"])],
-1),
samples)
ray.init(num_cpus=8, num_gpus=0)
DQNAgent = DQNTrainer(env="leduc_holdem", config=config)
DQNAgent.restore(checkpoint_path)
reward_sums = {a: 0 for a in env.possible_agents}
i = 0
env.reset()
for agent in env.agent_iter():
observation, reward, done, info = env.last()
obs = observation['observation']
reward_sums[agent] += reward
if done:
action = None
else:
print(DQNAgent.get_policy(agent))
policy = DQNAgent.get_policy(agent)
batch_obs = {
'obs': {
'observation': np.expand_dims(observation['observation'], 0),
'action_mask': np.expand_dims(observation['action_mask'], 0)
}
}
batched_action, state_out, info = policy.compute_actions_from_input_dict(
batch_obs)
single_action = batched_action[0]
action = single_action
env.step(action)
i += 1
env.render()