在单智体强化学习(single-RL)中,置信域方法(trust-region method)有两个比较典型的算法,分别是置信域策略优化算法Trust Region Policy Optimization (TRPO)以及近端策略优化算法Proximal Policy Optimization (PPO),他们在离散和连续RL问题上都表现出十分优越的性能。置信域方法的有效
In this research, we examine how to apply policy gradient method, Trust Region Policy Optimization (TRPO) to solve the hide and seek game environment. We also examine the configuration of TRPO algorithm that gives the best performance and its comparison with the Vanilla Policy Gradient (VPG) ...
To tackle this problem, we conduct a game-theoretical analysis in the policy space, and propose a multi-agent trust region learning method (MATRL), which enables trust region optimization for multi-agent learning. Specifically, MATRL finds a stable improvement direction that is guided by the ...
we conduct a game-theoretical analysis in the policy space, and propose a multi-agent trust region learning method (MATRL), which enables trust region optimization for multi-agent learning. Specifically, MATRL finds a stable improvement direction that is guided by the solution concept of Nash equ...
Kurutach, T., Clavera, I., Duan, Y., Tamar, A., Abbeel, P.: Model-ensemble trust-region policy optimization. In: International Conference on Learning Representations (2018) Google Scholar D’Oro, P., Jaśkowski, W.: How to learn a useful critic? Model-based action-gradient-estimator...
Multi-agent constrained policy optimisation In this section, we first present a theoretically-justified safe multi-agent policy iteration procedure that leverages multi-agent trust region learning and constrained policy optimisation to solve constrained Markov games. Based on this, we propose two practical...
Liu, B., Cai, Q., Yang, Z., Wang, Z.: Neural proximal/trust region policy optimization attains globally optimal policy (2019). arXiv preprint arXiv:1906.10306 Wang, L., Cai, Q., Yang, Z., Wang, Z.: Neural policy gradient methods: global optimality and rates of convergence (2019)...
2.Multi-Agent Constrained Policy Optimization (MACPO) MACPO算法是基于信任域优化(Trust Region Optimization)的一种安全强化学习方法,核心思想是通过策略的逐步更新在保证安全的前提下实现奖励的单调提升。该方法的主要特点包括: 信任域约束:在每次策略更新时,引入一个KL散度约束,确保新策略不会与当前策略偏离太远。
where rRM is the fraction of the RM with importance weights outside the trust region [1/C, C] and D is a parameter. The most notable hyperparameters used in our description of the MARL setup are the spatial resolution for the interpolation of the actions onto the grid (determined by ...
PPO作为on-policy算法,在多智能体环境下有着与其他off-policy的算法相比有着相同的采样效率,并在大多数场景下有着更好的表现。 2. IPPO和MAPPO的区别? 是critic网络中MAPPO使用的是全局观测,而IPPO使用的是部分观测。 3. 为什么论文“Is Independent Learning All You Need in the StarCraft Multi-Agent Challeng...