2381a50a70559340a0335288d648b4bb9a675588,slm_lab/agent/algorithm/dqn.py,HydraDQN,sample,#HydraDQN#,440

Before Change

    @lab_api
    def sample(self):
        """Samples one batch per environment"""
        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)
        batch = {"states": [], "next_states": []}
        for b in batches:
            batch["states"].append(b["states"])
            batch["next_states"].append(b["next_states"])
        batch["batches"] = batches
        return batch

    def compute_q_target_values(self, batch):

After Change

        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)
        // collect per body for feedforward to hydra heads
        batch = {

Instances