The MinePlanner benchmark can be extended and combined with other approaches, such as reinforcement learning, to create more comprehensive and challenging testbeds for developing truly autonomous agents by:
Incorporating Reinforcement Learning Environments: Integrate MinePlanner tasks into reinforcement learning environments like OpenAI Gym or DeepMind Lab to enable agents to interact with and learn from the Minecraft world. This integration allows for the training of agents using RL algorithms in the challenging and diverse scenarios provided by MinePlanner.
Hybrid Planning-Reinforcement Learning Approaches: Develop hybrid approaches that combine classical planning techniques from MinePlanner with reinforcement learning methods. By leveraging the strengths of both paradigms, agents can benefit from the structured planning capabilities of MinePlanner tasks while also learning adaptive and flexible behaviors through RL.
Multi-Task Learning: Extend the MinePlanner benchmark to include a diverse set of tasks that cover a wide range of planning challenges in Minecraft. By creating a multi-task learning environment, agents can learn generalizable skills and strategies that transfer across different scenarios, enhancing their overall performance and adaptability.
Curriculum Learning: Design a curriculum of tasks within MinePlanner that progressively increase in complexity and difficulty. Agents can follow this curriculum to gradually build up their skills and knowledge, starting from simple tasks and advancing to more complex challenges, facilitating more effective learning and skill acquisition.
Interactive Learning Environments: Create interactive learning environments within MinePlanner where agents can receive feedback and guidance during the planning process. By incorporating human feedback or teacher signals, agents can learn from demonstrations and corrections, accelerating their learning and improving their planning abilities.
By extending and combining MinePlanner with these approaches, researchers can create a rich and diverse set of environments for training and evaluating autonomous agents, pushing the boundaries of AI capabilities in complex and dynamic worlds like Minecraft.