retrieve_related_paper_subgraph_input_data = {
    "base_queries": ["diffusion model"],
    "base_github_url": "https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail",
    "base_method_text": {
        "arxiv_id": "2302.13335v4",
        "arxiv_url": "http://arxiv.org/abs/2302.13335v4",
        "title": "Diffusion Model-Augmented Behavioral Cloning",
        "authors": [
            "Shang-Fu Chen",
            "Hsiang-Chun Wang",
            "Ming-Hao Hsu",
            "Chun-Mao Lai",
            "Shao-Hua Sun",
        ],
        "published_date": "2023-02-26T15:40:09Z",
        "journal": "",
        "doi": "",
        "summary": "Imitation learning addresses the challenge of learning by observing an\nexpert's demonstrations without access to reward signals from environments.\nMost existing imitation learning methods that do not require interacting with\nenvironments either model the expert distribution as the conditional\nprobability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s,\na). Despite the simplicity of modeling the conditional probability with BC, it\nusually struggles with generalization. While modeling the joint probability can\nimprove generalization performance, the inference procedure is often\ntime-consuming, and the model can suffer from manifold overfitting. This work\nproposes an imitation learning framework that benefits from modeling both the\nconditional and joint probability of the expert distribution. Our proposed\nDiffusion Model-Augmented Behavioral Cloning (DBC) employs a diffusion model\ntrained to model expert behaviors and learns a policy to optimize both the BC\nloss (conditional) and our proposed diffusion model loss (joint). DBC\noutperforms baselines in various continuous control tasks in navigation, robot\narm manipulation, dexterous manipulation, and locomotion. We design additional\nexperiments to verify the limitations of modeling either the conditional\nprobability or the joint probability of the expert distribution, as well as\ncompare different generative models. Ablation studies justify the effectiveness\nof our design choices.",
        "github_url": "https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail",
        "main_contributions": "The paper introduces Diffusion Model-Augmented Behavioral Cloning (DBC), a novel imitation learning framework. DBC combines behavioral cloning (BC), which models the conditional probability of actions given states, with a diffusion model that captures the joint probability of expert state-action pairs. This approach aims to leverage the efficiency and stability of BC while improving generalization by incorporating the diffusion model as a learning objective.",
        "methodology": "DBC uses a diffusion model trained on expert demonstrations to guide policy learning. The policy is trained to optimize both the BC loss and a diffusion model loss. The diffusion model loss is based on the difference between the diffusion model's estimate of how well the policy's actions fit the expert distribution and how well the expert's actions fit the distribution.",
        "experimental_setup": "The method was evaluated on continuous control tasks including navigation (MAZE), robot arm manipulation (FETCH PICK), dexterous manipulation (HAND ROTATE), and locomotion (CHEETAH, WALKER, ANTREACH). Baselines included BC, Implicit BC (IBC), and Diffusion Policy. Performance was measured using success rate and return. Ablation studies were conducted to analyze the impact of hyperparameters and design choices.",
        "limitations": "The framework is designed to learn from expert trajectories without interacting with the environment and cannot learn from agent trajectories. The method inherits any biases exhibited by the expert demonstrators.",
        "future_research_directions": "Future work includes extending the method to incorporate agent data to allow for improvement when interacting with environments is possible. Mitigating biases inherited from expert demonstrators is also a direction for future research.",
    },
    "add_queries": ["vision"],
}
