基于深度强化学习的机器人视觉导航安全性评估python源码+使用说明.zip

# Simulation Framework ## Environment The environment contains n+1 agents. N of them are humans controlled by certain unknown policy. The other is robot and it's controlled by one known policy. The environment is built on top of OpenAI gym library, and has implemented two abstract methods. * reset(): the environment will reset positions for all the agents and return observation for robot. Observation for one agent is the observable states of all other agents. * step(action): taking action of the robot as input, the environment computes observation for each agent and call agent.act(observation) to get actions of agents. Then environment detects whether there is a collision between agents. If not, the states of agents will be updated. Then observation, reward, done will be returned. ## Agent Agent is a base class, and has two derived class of human and robot. Agent class holds all the physical properties of an agent, including position, velocity, orientation, policy and etc. * visibility: humans are always visible, but robot can be set to be visible or invisible * sensor: can be either visual input or coordinate input * kinematics: can be either holonomic (move in any direction) or unicycle (has rotation constraints) * act(observation): transform observation to state and pass it to policy ## Policy Policy takes state as input and output an action. Current available policies: * ORCA: compute collision-free velocity under the reciprocal assumption * CADRL: learn a value network to predict the value of a state and during inference it predicts action for the most important human * LSTM-RL: use lstm to encode the human states into one fixed-length vector * SARL: use pairwise interaction module to model human-robot interaction and use self-attention to aggregate humans' information * OM-SARL: extend SARL by encoding intra-human interaction with a local map ## State There are multiple definition of states in different cases. The state of an agent representing all the knowledge of environments is defined as JointState, and it's different from the state of the whole environment. * ObservableState: position, velocity, radius of one agent * FullState: position, velocity, radius, goal position, preferred velocity, rotation * DualState: concatenation of one agent's full state and one another agent's observable state * JoinState: concatenation of one agent's full state and all other agents' observable states ## Action There are two types of actions depending on what kinematics constraint the agent has. * ActionXY: (vx, vy) if kinematics == 'holonomic' * ActionRot: (velocity, rotation angle) if kinematics == 'unicycle'