PyPI 官网下载 | navsim_envs-2.10.7.tar.gz

# `navsim_envs` Package Tutorial `navsim_envs` is a package that wraps the environments for Reinforcement Learning. Current implementation has the following sims encapsulated: * Arora from UCF ## Installation `pip install --upgrade navsim_envs` ### Test the installation In order to test the install: * create an experiment folder, we use folder `~/exp` and change into this folder. * create a minimal yaml file with the following contents: ```yaml env_path: /path/to/Berlin_Walk_V2.x86_64 ``` Let us say you named this file `min_env_config.yml`. * Run the tests: `navsim_env_test min_env_config.yml`. If you are using `navsim` container then follow the instructions on the container page. ## How to use the Arora env If you only want to use `AroraGymEnv`, then either subclass it or use it as follows: ```python import navsim_envs import gym env_config = { "agent_car_physics": 0, "debug": False, "episode_max_steps": 1000, "env_gpu_id": 0, "env_path": "/path/to/Berlin_Walk_V2.x86_64", "goal": 0, "goal_distance": 50, "log_folder": "./env_log", "obs_mode": 0, "obs_height": 256, "obs_width": 256, "seed": 123, "start_from_episode": 1, "reward_for_goal": 50, "reward_for_no_viable_path": -50, "reward_step_mul": 0.1, "reward_collision_mul": 4, "reward_spl_delta_mul": 1, "save_actions": True, "save_vector_obs": True, "save_visual_obs": True, "show_visual": False, "task": 0, "timeout": 600, "traffic_vehicles": 0, } # Either use gym.make to create an env env = gym.make("arora-v0", env_config=env_config) # or use the AroraGymEnv constructor to create an env env = navsim_envs.env.AroraGymEnv(env_config) ``` If you want to use our `navsim` conda environment or `navsim` container then follow the instructions <insert_link_here>. ## Config Parameters TODO: Explain all the above parameters here from config dictionary ### Observation Mode * `0` - Vector - Returns \[Agent Position (3-x,y,z) ,Agent Velocity (3-x,y,z), Agent Rotation(4-x,y,z,w), Goal Position (3-x,y,z,w)] * `1` - Visual- Returns \[\[Raw Agent Camera]\(84,84,3), \[Depth Agent Camera]\(84,84,1), \[Segmentation Agent Camera]\(84,84,3)] * `2` - VectorVisual - Returns \[\[Raw Agent Camera]\(84,84,3), \[Depth Agent Camera]\(84,84,1), \[Segmentation Agent Camera]\(84,84,3), \[Agent Position (3-x,y,z), Agent Velocity (3-x,y,z), Agent Rotation (4-x,y,z,w), Goal Position (3-x,y,z,w)]] ### Segmentation Mode * `0` - Object Seg: Each gameobject in the scene is a unique color * `1` - Tag Seg: Gameobject colors are based on the tag assigned such that all objects with the same tag share a color. (E.g. Car, Tree, Buildings) * `2` - Layer Seg: Similar to tag segmentation but with the physics layers. Current layers (Default, Trees, Tree Colliders, Agent Vehicle, Autonomous Vehicle, Parked Vehicle) ### Task * `0` - PointNav - Agent is randomly placed along with a randomly place goal position. The agent must navigate to the goal position. * `1` - SimpleObjectNav1 - The Agent is place at a specified starting location (manually identified traffic intersection). Goal is a sedan 40m forward in a straight line of the agent. The goal is to reach that sedan. * `2` - ObjectNav - The Agent is randomly place and goal object is defined by the goal parameter. The agent must reach one instance of the goal object. E.g. The goal object is a sedan and there any multiple sedans in the scene. Reaching any of the sedans results in a success. ### Goal : Only relevant for SimpleObjectNav and ObjectNav * `0` - Tocus * `1` - sedan1 * `2` - Car1 * `3` - Car2 * `4` - City Bus * `5` - Sporty_Hatchback * `Else` - SEDAN ### Rewards #### Reward Values * `reward_for_goal` : For pointnav, the goal is the target position to complete the task. * `reward_for_ep` : Exploration points are randomly placed in the environment to reward exploration. * `reward_collision_mul` : This reward multiple is used to determine the reward upon collision are anything that is not a goal point or exploration point. This includes other cars, building, trees, etc. * `reward_for_no_viable_path` : The map is a tiled specified bounded by the values of env.unity_map_dims(). If the agent goes outside of this area and falls -15m below the environment area or enters an area outside of the navigable area then this reward is activated. This will also result in a reset. * `reward_step_mul` : This reward multiplier is used to determine the rewards given at every step in addition to any other reward recieved at the same step. * `reward_spl_delta_mul` : This reward multiplier is used to determine the reward as the agent reduces the current SPL to the goal #### Reward Specifications ``` spl_delta = spl_prev_step – spl_current_step #If spl_delta is positive: the agent is closer to the goal according to spl #If spl_delta is negative: the agent is further away from the goal according to spl spl_reward = -(1 * reward_spl_delta_mul) if delta==0 else spl_delta * reward_spl_delta_mul step_reward = -(reward_for_goal / start_spl) * reward_step_mul collision_reward = reward_collision_mul * step_reward if `agent reached goal`: total_reward = goal_reward elif `agent has no viable path`: total reward = -no_viable_path_reward else: total_reward = spl_reward + step_reward + collision_reward ``` ### Agent Car Physics * `0` - Simple : Collisions and gravity only - An agent that moves by a specific distance and direction scaled by the provided action. This agent only experiences collision and gravity forces * `1` - Intermediate 1 : Addition of wheel torque * `2` - Intermediate 2 : Addition of suspension, downforce, and sideslip * `10` - Complex : Addition of traction control and varying surface friction ## Action Space [Throttle, Steering, Brake] * `Throttle` : -1.0 to 1.0 : Moves the agent backward or forward * `Steering` : -1.0 to 1.0 : Turns the steering column of the vehicle towards left or right * `Brake` : -1.0 to 1.0 : Reduces the agents current velocity ### Car motion explanation based on action space #### Simple Car Physics (Agent car physics `0`) In this mode, the steering and travel of a car is imitated without driven wheels. This means that the car will have a turning radius, but there is no momentum or acceleration that is experienced from torque being applied to wheels as in a real car. * `[0, 0, 0]` - No throttle, steering, or braking is applied. No agent travel. * `[1, 0, 0]` - Full forward throttle is applied. The agent travels forward at max velocity for the duration of the step. * `[0, -1, 0]` - No throttle or braking is applied. Steering is applied as a full left-turn, but because the forward/backward speed is zero, there is no travel. * `[1, -1, 0]` - Full forward throttle and full left-turn steering are applied. The agent travels forward at a leftward angle that is equal to a fraction of the max steering angle (25 degrees for the default car). This fraction is dependent on the length of the step in real time. * `[-1, -1, 0]` - Full backward throttle and full left-turn steering are applied. Similar to previous example, but with backward travel. * `[0.5, 0.5, 0]` - Half forward throttle and half right-turn steering are applied. The agent travels forward at half its max velocity and at a lesser rightward angle. * `[1, 0, 1]` - Full forward throttle and full braking are applied. These cancel each other out and result in no agent travel. * `[1, 0, 0.5]` - Full forward throttle and half braking are applied. The agent travels forward at half throttle. * `[0, 0, 1]` - Full braking is applied, with no throttle or steering. No agent travel. #### Torque-Driven Car Physics (Agent car physics `>0`) The agent car is driven forward by applying torque to each drive wheel. The agent will have momentum, so travel is poss