python3 soft_actor_critic train
With some arguments:
python3 soft_actor_critic train --env-name MountainCarContinuous-v0 --learning-rate 0.001
python3 soft_actor_critic eval --run-name "name_of_my_last_run"
With some arguments:
python3 soft_actor_critic eval --run-name name_of_my_last_run --hidden-units 512 512 --seed 2
(The environment name, and the hidden units need to correspond to the arguments in the run that is loaded)
python soft_actor_critic --help
Output:
usage: Use "python soft_actor_critic --help" for more information
PyTorch Soft Actor-Critic
positional arguments:
{train,eval} Selection of the mode to perform
train Train an agent
eval Evaluate the performance of an already trained agent
optional arguments:
-h, --help show this help message and exit
python soft_actor_critic train --help
Output:
usage: Use "python soft_actor_critic --help" for more information train [-h] [--env-name] [--hidden-units [...]]
[--directory] [--seed] [--run-name]
[--batch-size] [--memory-size]
[--learning-rate] [--gamma] [--tau]
[--num-steps] [--start-step] [--alpha]
optional arguments:
-h, --help show this help message and exit
--env-name Gym environment to train on (default: LunarLanderContinuous-v2)
--hidden-units [ ...]
List of networks' hidden units (default: [256, 256])
--directory Root directory in which the run folder will be created (default: ../runs/)
--seed Seed used for pytorch, numpy and the environment (default: 1)
--run-name Name used for saving the weights and the logs (default: generated using the "get_run_name"
function)
--batch-size Batch size used by the agent during the learning phase (default: 256)
--memory-size Size of the replay buffer (default: 1000000)
--learning-rate Learning rate used for the networks and entropy optimization (default: 0.0003)
--gamma Discount rate used by the agent (default: 0.99)
--tau Value used for the progressive update of the target networks (default: 0.005)
--num-steps Number training steps (default: 1000000)
--start-step Step after which the agent starts to learn (default: 1000)
--alpha Starting value of the entropy (alpha) (default: 0.2)
python soft_actor_critic eval --help
Output:
usage: Use "python soft_actor_critic --help" for more information eval [-h] [--env-name] [--hidden-units [...]]
[--directory] [--seed] [--run-name]
[--num-episodes] [--deterministic] [--render]
[--record]
optional arguments:
-h, --help show this help message and exit
--env-name Gym environment to train on (default: LunarLanderContinuous-v2)
--hidden-units [ ...]
List of networks' hidden units (default: [256, 256])
--directory Root directory in which the run folder will be created (default: ../runs/)
--seed Seed used for pytorch, numpy and the environment (default: 1)
--run-name Run name of an already trained agent located in the "--directory" directory
--num-episodes Number of episodes to run (default: 3)
--deterministic Toggle deterministic behavior of the agent when interacting with the environment
--render Toggle the rendering of the episodes
--record Toggle the recording of the episodes (toggling "record" would also toggle "render")
This is an attempt to show the equation of the paper, and their correspondence in the source code:
def _critic_optimization(self, state: torch.Tensor, action: torch.Tensor, reward: torch.Tensor,
next_state: torch.Tensor, done: torch.Tensor) -> Tuple[float, float]:
with torch.no_grad():
next_action, next_log_pi = self.policy.evaluate(next_state)
next_q_target_1, next_q_target_2 = self.target_critic.forward(next_state, next_action)
min_next_q_target = torch.min(next_q_target_1, next_q_target_2)
next_q = reward + (1 - done) * self.gamma * (min_next_q_target - self.alpha * next_log_pi)
q_1, q_2 = self.critic.forward(state, action)
q_network_1_loss = F.mse_loss(q_1, next_q)
q_network_2_loss = F.mse_loss(q_2, next_q)
q_loss = (q_network_1_loss + q_network_2_loss) / 2
self.critic_optimizer.zero_grad()
q_loss.backward()
self.critic_optimizer.step()
return q_network_1_loss.item(), q_network_2_loss.item()def _policy_optimization(self, state: torch.Tensor) -> float:
with eval_mode(self.critic):
predicted_action, log_probabilities = self.policy.evaluate(state)
q_1, q_2 = self.critic(state, predicted_action)
min_q = torch.min(q_1, q_2)
policy_loss = ((self.alpha * log_probabilities) - min_q).mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
return policy_loss.item()def _entropy_optimization(self, state: torch.Tensor) -> float:
with eval_mode(self.policy):
_, log_pi = self.policy.evaluate(state)
alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
self.alpha = self.log_alpha.exp()
return alpha_loss.item()List of repository that helped me to solve technical issues:
Equations made with this tool, taken from this thread.
Soft Actor-Critic Algorithms and Applications, Haarnoja et al. [arxiv] (January 2019)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja et al. [arxiv] (January 2018)
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