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In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet of Vehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in the crowded parking lots. In such scenarios the vehicles have to rely on the local observations and exhibit cooperative behavior to ensure safe and efficient trips. This type of problems can be abstracted to the socalled multi-agent pathfinding when a group of agents, confined to a graph, have to find collision-free paths to their goals (ideally, minimizing an objective function e.g. travel time). Widely used algorithms for solving this problem rely on the assumption that a central controller exists for which the full state of the environment (i.e. the agents current positions, their targets, configuration of the static obstacles etc.) is known and they can not be straightforwardly be adapted to the partially-observable setups. To this end, we suggest a novel approach which is based on the decomposition of the problem into the two sub-tasks: reaching the goal and avoiding the collisions. To accomplish each of this task we utilize reinforcement learning methods such as Deep Monte Carlo Tree Search, Q-mixing networks, and policy gradients methods to design the policies that map the agents' observations to actions. Next, we introduce the policy-mixing mechanism to end up with a single hybrid policy that allows each agent to exhibit both types of behavior -the individual one (reaching the goal) and the cooperative one (avoiding the collisions with other agents). We conduct an extensive empirical evaluation that shows that the suggested hybrid-policy outperforms standalone stat-ofthe-art reinforcement learning methods for this kind of problems by a notable margin.

Currently, deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. Often these results are achieved at the expense of huge computational costs and require an incredible number of episodes of interaction between the agent and the environment. There are two main approaches to improving the sample efficiency of reinforcement learning methods -using hierarchical methods and expert demonstrations. In this paper, we propose a combination of these approaches that allow the agent to use low-quality demonstrations in complex vision-based environments with multiple related goals. Our forgetful experience replay (ForgER) algorithm effectively handles errors in expert data and reduces quality losses when adapting the action space and states representation to the agent's capabilities. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. Our method is universal and can be integrated into various off-policy methods. It surpasses all known existing state-of-the-art RL methods using expert demonstrations on various model environments. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.Preprint. Under review.

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