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prev_text_actions = [] |
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for agent_id in next_info.agents: |
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agent_brain_info = self.training_buffer[agent_id].last_brain_info |
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agent_index = agent_brain_info.agents.index(agent_id) |
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visual_observations[i].append( |
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agent_brain_info.visual_observations[i][agent_brain_info.agents.index(agent_id)]) |
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vector_observations.append(agent_brain_info.vector_observations[agent_brain_info.agents.index(agent_id)]) |
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text_observations.append(agent_brain_info.text_observations[agent_brain_info.agents.index(agent_id)]) |
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visual_observations[i].append(agent_brain_info.visual_observations[i][agent_index]) |
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vector_observations.append(agent_brain_info.vector_observations[agent_index]) |
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text_observations.append(agent_brain_info.text_observations[agent_index]) |
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memories.append(agent_brain_info.memories[agent_brain_info.agents.index(agent_id)]) |
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rewards.append(agent_brain_info.rewards[agent_brain_info.agents.index(agent_id)]) |
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local_dones.append(agent_brain_info.local_done[agent_brain_info.agents.index(agent_id)]) |
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max_reacheds.append(agent_brain_info.max_reached[agent_brain_info.agents.index(agent_id)]) |
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agents.append(agent_brain_info.agents[agent_brain_info.agents.index(agent_id)]) |
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prev_vector_actions.append( |
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agent_brain_info.previous_vector_actions[agent_brain_info.agents.index(agent_id)]) |
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prev_text_actions.append(agent_brain_info.previous_text_actions[agent_brain_info.agents.index(agent_id)]) |
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memories.append(agent_brain_info.memories[agent_index]) |
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rewards.append(agent_brain_info.rewards[agent_index]) |
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local_dones.append(agent_brain_info.local_done[agent_index]) |
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max_reacheds.append(agent_brain_info.max_reached[agent_index]) |
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agents.append(agent_brain_info.agents[agent_index]) |
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prev_vector_actions.append(agent_brain_info.previous_vector_actions[agent_index]) |
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prev_text_actions.append(agent_brain_info.previous_text_actions[agent_index]) |
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agents, |
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local_dones, prev_vector_actions, prev_text_actions, max_reacheds) |
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agents, local_dones, prev_vector_actions, prev_text_actions, max_reacheds) |
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return curr_info |
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def generate_intrinsic_rewards(self, curr_info, next_info): |
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Arthur Juliani