in tutorial/deprecated/tutorial_reinforce_with_evaluation_s/reinforce.py [0:0]
def run(self):
# Instantiate the learning model abd the baseline model
action_model = ActionModel(self.obs_dim, self.n_actions, 16)
baseline_model = BaselineModel(self.obs_dim, 16)
self.learning_model = Model(action_model, baseline_model)
self.agent = self._create_agent(
n_actions=self.n_actions, model=self.learning_model
)
# We create a batcher dedicated to evaluation
model = copy.deepcopy(self.learning_model)
self.evaluation_batcher = Batcher(
n_timesteps=self.config["max_episode_steps"],
create_agent=self._create_agent,
create_env=self._create_env,
env_args={
"n_envs": self.config["n_evaluation_envs"],
"max_episode_steps": self.config["max_episode_steps"],
"env_name": self.config["env_name"],
},
agent_args={"n_actions": self.n_actions, "model": model},
n_threads=self.config["n_evaluation_threads"],
seeds=[
self.config["env_seed"] + k * 10
for k in range(self.config["n_evaluation_threads"])
],
agent_info=DictTensor({"stochastic": torch.tensor([True])}),
env_info=DictTensor({}),
)
# Creation of the batcher for sampling complete episodes (i.e Episode Batcher)
# The batcher will sample n_threads*n_envs trajectories at each call
# To have a fast batcher, we have to configure it with n_timesteps=self.config["max_episode_steps"]
model = copy.deepcopy(self.learning_model)
self.train_batcher = Batcher(
n_timesteps=self.config["max_episode_steps"],
create_agent=self._create_agent,
create_env=self._create_env,
env_args={
"n_envs": self.config["n_envs"],
"max_episode_steps": self.config["max_episode_steps"],
"env_name": self.config["env_name"],
},
agent_args={"n_actions": self.n_actions, "model": model},
n_threads=self.config["n_threads"],
seeds=[
self.config["env_seed"] + k * 10
for k in range(self.config["n_threads"])
],
agent_info=DictTensor({"stochastic": torch.tensor([True])}),
env_info=DictTensor({}),
)
# Creation of the optimizer
optimizer = torch.optim.RMSprop(
self.learning_model.parameters(), lr=self.config["lr"]
)
# Training Loop:
_start_time = time.time()
self.iteration = 0
# We launch the evaluation batcher (in deterministic mode)
n_episodes = (
self.config["n_evaluation_threads"] * self.config["n_evaluation_envs"]
)
agent_info = DictTensor(
{"stochastic": torch.tensor([False]).repeat(n_episodes)}
)
self.evaluation_batcher.reset(agent_info=agent_info)
self.evaluation_batcher.execute()
self.evaluation_iteration = self.iteration
while time.time() - _start_time < self.config["time_limit"]:
# Update the batcher with the last version of the learning model
self.train_batcher.update(self.learning_model.state_dict())
# Call the batcher to get a sample of trajectories
# 1) The policy will be executed in "stochastic' mode
n_episodes = self.config["n_envs"] * self.config["n_threads"]
agent_info = DictTensor(
{"stochastic": torch.tensor([True]).repeat(n_episodes)}
)
self.train_batcher.reset(agent_info=agent_info)
self.train_batcher.execute()
# 2) We get the trajectories (and wait until the trajectories have been sampled)
trajectories, n_env_running = self.train_batcher.get(blocking=True)
assert n_env_running == 0
# 3) Now, we compute the loss
dt = self.get_loss(trajectories)
[self.logger.add_scalar(k, dt[k].item(), self.iteration) for k in dt.keys()]
# Computation of final loss
ld = self.config["baseline_coef"] * dt["baseline_loss"]
lr = self.config["reinforce_coef"] * dt["reinforce_loss"]
le = self.config["entropy_coef"] * dt["entropy_loss"]
floss = ld - le - lr
optimizer.zero_grad()
floss.backward()
optimizer.step()
# Update the train batcher with the updated model
self.train_batcher.update(self.learning_model.state_dict())
print(
"At iteration %d, avg (discounted) reward is %f"
% (self.iteration, dt["avg_reward"].item())
)
print(
"\t Avg trajectory length is %f"
% (trajectories.trajectories.lengths.float().mean().item())
)
print(
"\t Curves can be visualized using 'tensorboard --logdir=%s'"
% self.config["logdir"]
)
self.iteration += 1
# We check the evaluation batcher
evaluation_trajectories, n_env_running = self.evaluation_batcher.get(
blocking=False
)
if not evaluation_trajectories is None: # trajectories are available
assert n_env_running == 0
# Compute the cumulated reward
cumulated_reward = (
(
evaluation_trajectories.trajectories["_observation/reward"]
* evaluation_trajectories.trajectories.mask()
)
.sum(1)
.mean()
)
self.logger.add_scalar(
"evaluation_reward",
cumulated_reward.item(),
self.evaluation_iteration,
)
print(
"-- Iteration ",
self.iteration,
" Evaluation reward = ",
cumulated_reward.item(),
)
# We reexecute the evaluation batcher (with same value of agent_info and same number of episodes)
self.evaluation_batcher.update(self.learning_model.state_dict())
self.evaluation_iteration = self.iteration
n_episodes = (
self.config["n_evaluation_threads"]
* self.config["n_evaluation_envs"]
)
agent_info = DictTensor(
{"stochastic": torch.tensor([False]).repeat(n_episodes)}
)
self.evaluation_batcher.reset(agent_info=agent_info)
self.evaluation_batcher.execute()
self.train_batcher.close()
self.evaluation_batcher.get() # To wait for the last trajectories
self.evaluation_batcher.close()
self.logger.update_csv() # To save as a CSV file in logdir
self.logger.close()