基于上述动机,本文提出了一种简单而有效的基于模型的强化学习方法,即Model-Based Policy Optimization(MBPO),它使用短的模型生成的轨迹来进行策略优化。 MBPO 的关键思想:通过从真实数据中生成的短期模型回溯,避免了模型误差的累积,并且这种方法不需要复杂的模型生成过程。 MBPO 的优点: 超越了现有
Proximal Policy Optimization (PPO)详解 文章目录 On-policy v.s. Off-policy 将On-policy变为Off-policy PPO算法/TRPO算法 PPO2 总结 On-policy v.s. Off-policy On-Policy方式指的是用于学习的agent与观察环境的agent是同一个,所以参数θ始终保持一致。 Off-Policy方式指的是用于学习的agent与用于观察环境的...
Model-based policy optimization with deep reinforcement learning 本文使用ensemble方式构建模型组 {p^1_\theta,...,p^B_\theta } , 其中每一个模型都建模为高斯分布 p^i_\theta(s_{t+1},r|s_t,a_t)=N(u_\theta^i(s_t,a_t),\Sigma^i_\theta(s_t,a_t)) 单个的概率模型能够捕捉到偶然...
作者提出Model-based Offline Policy Optimization (MOPO)算法,用model based的方法来做offline RL,同时通过给reward添加惩罚项(soft reward penalty)来描述环境转移的不确定性(applying them with rewards artificially penalized by the uncertainty of the dynamics.)这种方式相当于在泛化性和风险之间做tradeoff。作者的意...
文章要点:文章简单理论分析了一下model-based RL的单调收敛,然后做实验验证生成很多的短的rollouts会有比较好的效果(using short model-generated rollouts branched from real data has the benefits)。 具体的,文章提出了一个model-based policy optimization (MBPO),其实这个算法和别的方法没啥大区别,就是trajector...
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate ...
We use a symbolic dynamics model to generate trajectories in model-based policy optimization to improve the sample efficiency of the learning algorithm. We evaluate our approach across various tasks within simulated environments. Our method demonstrates superior sample efficiency in these tasks compared ...
This paper explores the combination of model-based methods and multi-agent reinforcement learning (MARL) for more efficient coordination among multiple agents. A decentralized model-based MARL method, Policy Optimization with Dynamic Dependence Modeling
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Code to reproduce the experiments in MOPO: Model-based Offline Policy Optimization. Installation Install MuJoCo 2.0 at ~/.mujoco/mujoco200 and copy your license key to ~/.mujoco/mjkey.txt Create a conda environment and install mopo cd mopo conda env create -f environment/gpu-env.yml conda ac...