From 8beae424b2d34a84182738eae71480b0cb2d4372 Mon Sep 17 00:00:00 2001 From: Matteo Bettini Date: Sat, 9 Sep 2023 16:45:04 +0100 Subject: [PATCH] Typo in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5edbe892..d3482b04 100644 --- a/README.md +++ b/README.md @@ -349,7 +349,7 @@ To create a fake screen you need to have `Xvfb` installed. | `ball_passage.py` | This is the same as `joint_passage.py`, except now the agents are not connected by linkages and need to push a ball through the passage. The reward is only dependent on the ball and it's shaped to guide it through the passage. | drawing | | `ball_trajectory.py` | This is the same as `circle_trajectory.py` except the trajectory reward is now dependent on a ball object. Two agents need to drive the ball in a circular trajectory. If `joints=True` the agents are connected to the ball with linkages. | drawing | | `buzz_wire.py` | Two agents are connected to a mass through linkages and need to play the [Buzz Wire game](https://en.wikipedia.org/wiki/Wire_loop_game) in a straight corridor. Be careful not to touch the borders, or the episode ends! | drawing | -| `multi_give_way.py` | This scenario is an extension of `give_way.py` where four agents have to reach their goal by giving way to each other. | drawing | +| `multi_give_way.py` | This scenario is an extension of `give_way.py` where four agents have to reach their goal by giving way to each other. | drawing | | `navigation.py` | Randomly spawned agents need to navigate to their goal. Collisions can be turned on and agents can use LIDARs to avoid running into each other. Rewards can be shared or individual. Apart from position, velocity, and lidar readings, each agent can be set up to observe just the relative distance to its goal, or its relative distance to *all* goals (in this case the task needs heterogeneous behavior to be solved). The scenario can also be set up so that multiple agents share the same goal. | drawing | | `sampling.py` | `n_agents` are spawned randomly in a workspace with an underlying gaussian density function composed of `n_gaussians` modes. Agents need to collect samples by moving in this field. The field is discretized to a grid and once an agent visits a cell its sample is collected without replacement and given as reward to the whole team (or just to the agent if `shared_rew=False`). Agents can use a lidar to sens each other. Apart from lidar, position and velocity observations, each agent observes the values of samples in the 3x3 grid around it. | drawing | | `wind_flocking.py` | Two agents need to flock at a specified distance northwards. They are rewarded for their distance and the alignment of their velocity vectors to the reference. The scenario presents wind from north to south. The agents present physical heterogeneity: the smaller one has some aerodynamical properties and can shield the bigger one from wind, thus optimizing the flocking performance. Thus, the optimal solution to this task consists in the agents performing heterogeneous wind shielding. See the [SND paper](https://matteobettini.github.io/publication/system-neural-diversity-measuring-behavioral-heterogeneity-in-multi-agent-learning/) for more info. | drawing |