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add Munchhausen DQN refactoring

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romue 2021-06-21 10:42:35 +02:00
parent e541e34270
commit bd63c603ee
1 changed files with 13 additions and 8 deletions
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21
algorithms/_base.py
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@ -206,6 +206,14 @@ class MDQN(BaseQlearner):
self.alpha = alpha
self.clip0 = clip_l0
def tau_ln_pi(self, qs):
# Custom log-sum-exp trick from page 18 to compute the e log-policy terms
v_k = qs.max(-1)[0].unsqueeze(-1)
advantage = qs - v_k
logsum = torch.logsumexp(advantage / self.temperature, -1).unsqueeze(-1)
tau_ln_pi = advantage - self.temperature * logsum
return tau_ln_pi
def train(self):
if len(self.buffer) < self.batch_size: return
for _ in range(self.n_grad_steps):
@ -213,17 +221,14 @@ class MDQN(BaseQlearner):
experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
q_target_next = self.target_q_net(experience.next_observation).detach()
advantages_next = (q_target_next - q_target_next.max(-1)[0].unsqueeze(-1))
logsum = torch.logsumexp(advantages_next / self.temperature, -1).unsqueeze(-1)
tau_log_pi_next = advantages_next - self.temperature * logsum
tau_log_pi_next = self.tau_ln_pi(q_target_next)
q_k_targets = self.target_q_net(experience.observation).detach()
log_pi = self.tau_ln_pi(q_k_targets)
pi_target = F.softmax(q_target_next / self.temperature, dim=-1)
q_target = (self.gamma * (pi_target * (q_target_next - tau_log_pi_next) * (1 - experience.done)).sum(-1)).unsqueeze(-1)
q_k_targets = self.target_q_net(experience.observation).detach()
v_k_target = q_k_targets.max(-1)[0].unsqueeze(-1)
logsum = torch.logsumexp((q_k_targets - v_k_target) / self.temperature, -1).unsqueeze(-1)
log_pi = q_k_targets - v_k_target - self.temperature * logsum
munchausen_addon = log_pi.gather(-1, experience.action)
munchausen_reward = (experience.reward + self.alpha * torch.clamp(munchausen_addon, min=self.clip0, max=0))
@ -272,5 +277,5 @@ if __name__ == '__main__':
dqn, target_dqn = BaseDQN(), BaseDQN()
learner = MDQN(dqn, target_dqn, env, BaseBuffer(40000), target_update=3500, lr=0.0008, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
train_every_n_steps=4, eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64)
train_every_n_steps=4, eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64)
learner.learn(100000)