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hacky changes to transport for online change #31

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86 changes: 71 additions & 15 deletions vmas/scenarios/transport.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@

class Scenario(BaseScenario):
def make_world(self, batch_dim: int, device: torch.device, **kwargs):
n_agents = kwargs.get("n_agents", 2)
self.n_agents = kwargs.get("n_agents", 2)

# general world settings
self.world_semidim = kwargs.get("world_semidim", 1.0) # m
Expand Down Expand Up @@ -65,9 +65,11 @@ def make_world(self, batch_dim: int, device: torch.device, **kwargs):
self.time_penalty = kwargs.get("time_penalty", 0.0)

# capabilities
self.capability_mult_range = kwargs.get("capability_mult_range", [0.5, 2])
self.capability_mult_min = self.capability_mult_range[0]
self.capability_mult_max = self.capability_mult_range[1]
self.iter_ct = 0
self.agent_0_failed = False
# self.capability_mult_range = kwargs.get("capability_mult_range", [0.5, 2])
# self.capability_mult_min = self.capability_mult_range[0]
# self.capability_mult_max = self.capability_mult_range[1]
self.capability_representation = kwargs.get("capability_representation", "raw")

# Make world
Expand All @@ -86,12 +88,19 @@ def make_world(self, batch_dim: int, device: torch.device, **kwargs):
)

# Add agents
print("make world called")
capabilities = [] # save capabilities for relative capabilities later
for i in range(n_agents):
max_linear_vel = self.default_agent_max_linear_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
max_angular_vel = self.default_agent_max_angular_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
radius = self.default_agent_radius * random.uniform(self.capability_mult_min, self.capability_mult_max)
mass = self.default_agent_mass * random.uniform(self.capability_mult_min, self.capability_mult_max)
for i in range(self.n_agents):
if i == 0:
max_linear_vel = self.default_agent_max_linear_vel * 1.0
max_angular_vel = self.default_agent_max_angular_vel * 1.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0
else:
max_linear_vel = self.default_agent_max_linear_vel * 1.0
max_angular_vel = self.default_agent_max_angular_vel * 1.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0

capabilities.append([max_linear_vel, max_angular_vel, radius, mass])

Expand Down Expand Up @@ -139,11 +148,26 @@ def reset_world_at(self, env_index: int = None):
# only do this during batched resets!
if not env_index:
capabilities = [] # save capabilities for relative capabilities later
for agent in self.world.agents:
max_linear_vel = self.default_agent_max_linear_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
max_angular_vel = self.default_agent_max_angular_vel * random.uniform(self.capability_mult_min, self.capability_mult_max)
radius = self.default_agent_radius * random.uniform(self.capability_mult_min, self.capability_mult_max)
mass = self.default_agent_mass * random.uniform(self.capability_mult_min, self.capability_mult_max)
for i in range(self.n_agents):
agent = self.world.agents[i]

if i == 0:
if self.agent_0_failed:
max_linear_vel = self.default_agent_max_linear_vel * 0.0
max_angular_vel = self.default_agent_max_angular_vel * 0.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0
agent.color = (1.0, 0.0, 0.0)
else:
max_linear_vel = self.default_agent_max_linear_vel * 1.0
max_angular_vel = self.default_agent_max_angular_vel * 1.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0
else:
max_linear_vel = self.default_agent_max_linear_vel * 1.0
max_angular_vel = self.default_agent_max_angular_vel * 1.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0

capabilities.append([max_linear_vel, max_angular_vel, radius, mass])

Expand All @@ -168,7 +192,7 @@ def reset_world_at(self, env_index: int = None):
self.world,
env_index,
min_dist_between_entities=max(
package.shape.circumscribed_radius() + goal.shape.radius + 0.01
package.shape.circumscribed_radius() + goal.shape.radius + 1.0
for package in self.packages
),
x_bounds=(
Expand Down Expand Up @@ -283,6 +307,37 @@ def reward(self, agent: Agent):
distance <= self.min_collision_distance
] += self.interagent_collision_penalty

agent_0 = self.world.agents[0]
agent_1 = self.world.agents[1]
env_0_dist_to_pkg = torch.linalg.vector_norm(agent_0.state.pos - package.state.pos, dim=-1)[0]
print("dist to pkg", env_0_dist_to_pkg)
if env_0_dist_to_pkg < 1.2:
self.agent_0_failed = True

capabilities = [] # save capabilities for relative capabilities later
for i in range(self.n_agents):
agent = self.world.agents[i]

if i == 0:
max_linear_vel = self.default_agent_max_linear_vel * 0.0
max_angular_vel = self.default_agent_max_angular_vel * 0.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0
agent.color = (1.0, 0.0, 0.0)
else:
max_linear_vel = self.default_agent_max_linear_vel * 1.0
max_angular_vel = self.default_agent_max_angular_vel * 1.0
radius = self.default_agent_radius * 1.0
mass = self.default_agent_mass * 1.0

capabilities.append([max_linear_vel, max_angular_vel, radius, mass])

agent.u_multiplier=[max_linear_vel, max_angular_vel]
agent.shape=Sphere(radius)
agent.mass=mass

self.capabilities = torch.tensor(capabilities)

# reward for how close agents are to all packages
if self.add_dense_reward:
for i, package in enumerate(self.packages):
Expand Down Expand Up @@ -460,6 +515,7 @@ def default_observation(self, agent: Agent):
)

def observation(self, agent: Agent):
self.iter_ct += 1
if self.partial_observations:
return self.partial_observation(agent)
else:
Expand Down