Implement Search & Rescue Multi-Agent Environment

docs/api/environments/search_and_rescue.md

@@ -0,0 +1,11 @@
+::: jumanji.environments.swarms.search_and_rescue.env.SearchAndRescue
+    selection:
+      members:
+        - __init__
+        - reset
+        - step
+        - observation_spec
+        - action_spec
+        - reward_spec
+        - render
+        - animate

docs/environments/search_and_rescue.md

@@ -0,0 +1,67 @@
+# 🚁 Search & Rescue
+
+[//]: # (TODO: Add animated plot)
+
+Multi-agent environment, modelling a group of agents searching the environment
+for multiple targets. Agents are individually rewarded for finding a target
+that has not previously been detected.
+
+Each agent visualises a local region around it, creating a simple segmented view
+of locations of other agents in the vicinity. The environment is updated in the
+following sequence:
+
+- The velocity of searching agents are updated, and consequently their positions.
+- The positions of targets are updated.
+- Agents are rewarded for being within a fixed range of targets, and the target
+  being within its view cone.
+- Targets within detection range and an agents view cone are marked as found.
+- Local views of the environment are generated for each search agent.
+
+The agents are allotted a fixed number of steps to locate the targets. The search
+space is a uniform space with unit dimensions, and wrapped at the boundaries.
+
+## Observations
+
+- `searcher_views`: jax array (float) of shape `(num_searchers, num_vision)`. Each agent
+  generates an independent observation, an array of values representing the distance
+  along a ray from the agent to the nearest neighbour, with  each cell representing a
+  ray angle (with `num_vision` rays evenly distributed over the agents field of vision).
+  For example if an agent sees another agent straight ahead and `num_vision = 5` then
+  the observation array could be
+
+  ```
+  [-1.0, -1.0, 0.5, -1.0, -1.0]
+  ```
+
+  where `-1.0` indicates there is no agents along that ray, and `0.5` is the normalised
+  distance to the other agent.
+- `targets_remaining`: float in the range `[0, 1]`. The normalised number of targets
+  remaining to be detected (i.e. 1.0 when no targets have been found).
+- `time_remaining`: float in the range `[0, 1]`. The normalised number of steps remaining
+  to locate the targets (i.e. 0.0 at the end of the episode).
+
+## Actions
+
+Jax array (float) of `(num_searchers, 2)` in the range `[-1, 1]`. Each entry in the
+array represents an update of each agents velocity in the next step. Searching agents
+update their velocity each step by rotating and accelerating/decelerating, where the
+values are `[rotation, acceleration]`. Values are clipped to the range `[-1, 1]`
+and then scaled by max rotation and acceleration parameters, i.e. the new values each
+step are given by
+
+```
+heading = heading + max_rotation * action[0]
+```
+
+and speed
+
+```
+speed = speed + max_acceleration * action[1]
+```
+
+Once applied, agent speeds are clipped to velocities within a fixed range of speeds.
+
+## Rewards
+
+Jax array (float) of `(num_searchers,)`. Rewards are generated for each agent individually.
+Agents are rewarded 1.0 for locating a target that has not already been detected.

jumanji/__init__.py

@@ -142,3 +142,10 @@
 # LevelBasedForaging  with a random generator with 8 grid size,
 # 2 agents and 2 food items and the maximum agent's level is 2.
 register(id="LevelBasedForaging-v0", entry_point="jumanji.environments:LevelBasedForaging")
+
+###
+# Swarm Environments
+###
+
+# Search-and-Rescue environment
+register(id="SearchAndRescue-v0", entry_point="jumanji.environments:SearchAndRescue")

jumanji/environments/__init__.py

@@ -59,6 +59,7 @@
 from jumanji.environments.routing.snake.env import Snake
 from jumanji.environments.routing.sokoban.env import Sokoban
 from jumanji.environments.routing.tsp.env import TSP
+from jumanji.environments.swarms.search_and_rescue.env import SearchAndRescue
 
 
 def is_colab() -> bool:

jumanji/environments/swarms/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 InstaDeep Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

jumanji/environments/swarms/common/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 InstaDeep Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

jumanji/environments/swarms/common/common_test.py

@@ -0,0 +1,193 @@
+# Copyright 2022 InstaDeep Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Tuple
+
+import jax
+import jax.numpy as jnp
+import matplotlib
+import matplotlib.pyplot as plt
+import pytest
+
+from jumanji.environments.swarms.common import types, updates, viewer
+
+
+@pytest.fixture
+def params() -> types.AgentParams:
+    return types.AgentParams(
+        max_rotate=0.5,
+        max_accelerate=0.01,
+        min_speed=0.01,
+        max_speed=0.05,
+        view_angle=0.5,
+    )
+
+
+@pytest.mark.parametrize(
+    "heading, speed, actions, expected",
+    [
+        [0.0, 0.01, [1.0, 0.0], (0.5 * jnp.pi, 0.01)],
+        [0.0, 0.01, [-1.0, 0.0], (1.5 * jnp.pi, 0.01)],
+        [jnp.pi, 0.01, [1.0, 0.0], (1.5 * jnp.pi, 0.01)],
+        [jnp.pi, 0.01, [-1.0, 0.0], (0.5 * jnp.pi, 0.01)],
+        [1.75 * jnp.pi, 0.01, [1.0, 0.0], (0.25 * jnp.pi, 0.01)],
+        [0.0, 0.01, [0.0, 1.0], (0.0, 0.02)],
+        [0.0, 0.01, [0.0, -1.0], (0.0, 0.01)],
+        [0.0, 0.02, [0.0, -1.0], (0.0, 0.01)],
+        [0.0, 0.05, [0.0, -1.0], (0.0, 0.04)],
+        [0.0, 0.05, [0.0, 1.0], (0.0, 0.05)],
+    ],
+)
+def test_velocity_update(
+    params: types.AgentParams,
+    heading: float,
+    speed: float,
+    actions: List[float],
+    expected: Tuple[float, float],
+) -> None:
+    key = jax.random.PRNGKey(101)
+
+    state = types.AgentState(
+        pos=jnp.zeros((1, 2)),
+        heading=jnp.array([heading]),
+        speed=jnp.array([speed]),
+    )
+    actions = jnp.array([actions])
+
+    new_heading, new_speed = updates.update_velocity(key, params, (actions, state))
+
+    assert jnp.isclose(new_heading[0], expected[0])
+    assert jnp.isclose(new_speed[0], expected[1])
+
+
+@pytest.mark.parametrize(
+    "pos, heading, speed, expected, env_size",
+    [
+        [[0.0, 0.5], 0.0, 0.1, [0.1, 0.5], 1.0],
+        [[0.0, 0.5], jnp.pi, 0.1, [0.9, 0.5], 1.0],
+        [[0.5, 0.0], 0.5 * jnp.pi, 0.1, [0.5, 0.1], 1.0],
+        [[0.5, 0.0], 1.5 * jnp.pi, 0.1, [0.5, 0.9], 1.0],
+        [[0.4, 0.2], 0.0, 0.2, [0.1, 0.2], 0.5],
+        [[0.1, 0.2], jnp.pi, 0.2, [0.4, 0.2], 0.5],
+        [[0.2, 0.4], 0.5 * jnp.pi, 0.2, [0.2, 0.1], 0.5],
+        [[0.2, 0.1], 1.5 * jnp.pi, 0.2, [0.2, 0.4], 0.5],
+    ],
+)
+def test_move(
+    pos: List[float], heading: float, speed: float, expected: List[float], env_size: float
+) -> None:
+    pos = jnp.array(pos)
+    new_pos = updates.move(pos, heading, speed, env_size)
+
+    assert jnp.allclose(new_pos, jnp.array(expected))
+
+
+@pytest.mark.parametrize(
+    "pos, heading, speed, actions, expected_pos, expected_heading, expected_speed, env_size",
+    [
+        [[0.0, 0.5], 0.0, 0.01, [0.0, 0.0], [0.01, 0.5], 0.0, 0.01, 1.0],
+        [[0.5, 0.0], 0.0, 0.01, [1.0, 0.0], [0.5, 0.01], 0.5 * jnp.pi, 0.01, 1.0],
+        [[0.5, 0.0], 0.0, 0.01, [-1.0, 0.0], [0.5, 0.99], 1.5 * jnp.pi, 0.01, 1.0],
+        [[0.0, 0.5], 0.0, 0.01, [0.0, 1.0], [0.02, 0.5], 0.0, 0.02, 1.0],
+        [[0.0, 0.5], 0.0, 0.01, [0.0, -1.0], [0.01, 0.5], 0.0, 0.01, 1.0],
+        [[0.0, 0.5], 0.0, 0.05, [0.0, 1.0], [0.05, 0.5], 0.0, 0.05, 1.0],
+        [[0.495, 0.25], 0.0, 0.01, [0.0, 0.0], [0.005, 0.25], 0.0, 0.01, 0.5],
+        [[0.25, 0.005], 1.5 * jnp.pi, 0.01, [0.0, 0.0], [0.25, 0.495], 1.5 * jnp.pi, 0.01, 0.5],
+    ],
+)
+def test_state_update(
+    params: types.AgentParams,
+    pos: List[float],
+    heading: float,
+    speed: float,
+    actions: List[float],
+    expected_pos: List[float],
+    expected_heading: float,
+    expected_speed: float,
+    env_size: float,
+) -> None:
+    key = jax.random.PRNGKey(101)
+
+    state = types.AgentState(
+        pos=jnp.array([pos]),
+        heading=jnp.array([heading]),
+        speed=jnp.array([speed]),
+    )
+    actions = jnp.array([actions])
+
+    new_state = updates.update_state(key, env_size, params, state, actions)
+
+    assert isinstance(new_state, types.AgentState)
+    assert jnp.allclose(new_state.pos, jnp.array([expected_pos]))
+    assert jnp.allclose(new_state.heading, jnp.array([expected_heading]))
+    assert jnp.allclose(new_state.speed, jnp.array([expected_speed]))
+
+
+def test_view_reduction() -> None:
+    view_a = jnp.array([-1.0, -1.0, 0.2, 0.2, 0.5])
+    view_b = jnp.array([-1.0, 0.2, -1.0, 0.5, 0.2])
+    result = updates.view_reduction(view_a, view_b)
+    assert jnp.allclose(result, jnp.array([-1.0, 0.2, 0.2, 0.2, 0.2]))
+
+
+@pytest.mark.parametrize(
+    "pos, view_angle, env_size, expected",
+    [
+        [[0.05, 0.0], 0.5, 1.0, [-1.0, -1.0, 0.5, -1.0, -1.0]],
+        [[0.0, 0.05], 0.5, 1.0, [0.5, -1.0, -1.0, -1.0, -1.0]],
+        [[0.0, 0.95], 0.5, 1.0, [-1.0, -1.0, -1.0, -1.0, 0.5]],
+        [[0.95, 0.0], 0.5, 1.0, [-1.0, -1.0, -1.0, -1.0, -1.0]],
+        [[0.05, 0.0], 0.25, 1.0, [-1.0, -1.0, 0.5, -1.0, -1.0]],
+        [[0.0, 0.05], 0.25, 1.0, [-1.0, -1.0, -1.0, -1.0, -1.0]],
+        [[0.0, 0.95], 0.25, 1.0, [-1.0, -1.0, -1.0, -1.0, -1.0]],
+        [[0.01, 0.0], 0.5, 1.0, [-1.0, 0.1, 0.1, 0.1, -1.0]],
+        [[0.0, 0.45], 0.5, 1.0, [4.5, -1.0, -1.0, -1.0, -1.0]],
+        [[0.0, 0.45], 0.5, 0.5, [-1.0, -1.0, -1.0, -1.0, 0.5]],
+    ],
+)
+def test_view(pos: List[float], view_angle: float, env_size: float, expected: List[float]) -> None:
+    state_a = types.AgentState(
+        pos=jnp.zeros((2,)),
+        heading=0.0,
+        speed=0.0,
+    )
+
+    state_b = types.AgentState(
+        pos=jnp.array(pos),
+        heading=0.0,
+        speed=0.0,
+    )
+
+    obs = updates.view(
+        None, (view_angle, 0.02), state_a, state_b, n_view=5, i_range=0.1, env_size=env_size
+    )
+    assert jnp.allclose(obs, jnp.array(expected))
+
+
+def test_viewer_utils() -> None:
+    f, ax = plt.subplots()
+    f, ax = viewer.format_plot(f, ax, (1.0, 1.0))
+
+    assert isinstance(f, matplotlib.figure.Figure)
+    assert isinstance(ax, matplotlib.axes.Axes)
+
+    state = types.AgentState(
+        pos=jnp.zeros((3, 2)),
+        heading=jnp.zeros((3,)),
+        speed=jnp.zeros((3,)),
+    )
+
+    quiver = viewer.draw_agents(ax, state, "red")
+
+    assert isinstance(quiver, matplotlib.quiver.Quiver)

jumanji/environments/swarms/common/types.py

@@ -0,0 +1,53 @@
+# Copyright 2022 InstaDeep Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from dataclasses import dataclass
+else:
+    from chex import dataclass
+
+import chex
+
+
+@dataclass(frozen=True)
+class AgentParams:
+    """
+    max_rotate: Max angle an agent can rotate during a step (a fraction of pi)
+    max_accelerate: Max change in speed during a step
+    min_speed: Minimum agent speed
+    max_speed: Maximum agent speed
+    view_angle: Agent view angle, as a fraction of pi either side of its heading
+    """
+
+    max_rotate: float
+    max_accelerate: float
+    min_speed: float
+    max_speed: float
+    view_angle: float
+
+
+@dataclass
+class AgentState:
+    """
+    State of multiple agents of a single type
+
+    pos: 2d position of the (centre of the) agents
+    heading: Heading of the agents (in radians)
+    speed: Speed of the agents
+    """
+
+    pos: chex.Array  # (num_agents, 2)
+    heading: chex.Array  # (num_agents,)
+    speed: chex.Array  # (num_agents,)