2381a50a70559340a0335288d648b4bb9a675588,slm_lab/agent/algorithm/dqn.py,MultitaskDQN,sample,#MultitaskDQN#,333

Before Change

    @lab_api
    def sample(self):
        // NOTE the purpose of multi-body is to parallelize and get more batch_sizes
        batches = [body.memory.sample() for body in self.agent.nanflat_body_a]
        // Package data into pytorch variables
        for batch_b in batches:
            util.to_torch_batch(batch_b, self.net.gpu)
        // Concat state
        combined_states = torch.cat(
            [batch_b["states"] for batch_b in batches], dim=1)
        combined_next_states = torch.cat(
            [batch_b["next_states"] for batch_b in batches], dim=1)

After Change

        Note that multitask"s bodies are parallelized copies with similar envs, just to get more batch sizes
        """
        batches = []
        for body in self.agent.nanflat_body_a:
            body_batch = body.memory.sample()
            // one-hot actions to calc q_targets
            if body.is_discrete:
                body_batch["actions"] = util.to_one_hot(body_batch["actions"], body.action_space.high)
            body_batch = util.to_torch_batch(body_batch, self.net.gpu)
            batches.append(body_batch)
        // Concat states at dim=1 for feedforward
        batch = {
            "states": torch.cat([body_batch["states"] for body_batch in batches], dim=1),
            "next_states": torch.cat([body_batch["next_states"] for body_batch in batches], dim=1),
        }

Instances