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Zhongwei Li
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skills/pufferlib/scripts/env_template.py
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340
skills/pufferlib/scripts/env_template.py
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#!/usr/bin/env python3
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"""
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PufferLib Environment Template
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This template provides a starting point for creating custom PufferEnv environments.
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Customize the observation space, action space, and environment logic for your task.
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"""
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import numpy as np
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import pufferlib
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from pufferlib import PufferEnv
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class MyEnvironment(PufferEnv):
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"""
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Custom PufferLib environment template.
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This is a simple grid world example. Customize it for your specific task.
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"""
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def __init__(self, buf=None, grid_size=10, max_steps=1000):
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"""
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Initialize environment.
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Args:
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buf: Shared memory buffer (managed by PufferLib)
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grid_size: Size of the grid world
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max_steps: Maximum steps per episode
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"""
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super().__init__(buf)
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self.grid_size = grid_size
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self.max_steps = max_steps
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# Define observation space
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# Option 1: Flat vector observation
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self.observation_space = self.make_space((4,)) # [x, y, goal_x, goal_y]
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# Option 2: Dict observation with multiple components
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# self.observation_space = self.make_space({
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# 'position': (2,),
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# 'goal': (2,),
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# 'grid': (grid_size, grid_size)
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# })
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# Option 3: Image observation
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# self.observation_space = self.make_space((grid_size, grid_size, 3))
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# Define action space
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# Option 1: Discrete actions
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self.action_space = self.make_discrete(4) # 0: up, 1: right, 2: down, 3: left
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# Option 2: Continuous actions
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# self.action_space = self.make_space((2,)) # [dx, dy]
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# Option 3: Multi-discrete actions
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# self.action_space = self.make_multi_discrete([3, 3]) # Two 3-way choices
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# Initialize state
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self.agent_pos = None
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self.goal_pos = None
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self.step_count = 0
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self.reset()
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def reset(self):
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"""
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Reset environment to initial state.
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Returns:
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observation: Initial observation
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"""
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# Reset state
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self.agent_pos = np.array([0, 0], dtype=np.float32)
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self.goal_pos = np.array([self.grid_size - 1, self.grid_size - 1], dtype=np.float32)
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self.step_count = 0
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# Return initial observation
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return self._get_observation()
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def step(self, action):
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"""
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Execute one environment step.
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Args:
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action: Action to take
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Returns:
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observation: New observation
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reward: Reward for this step
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done: Whether episode is complete
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info: Additional information
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"""
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self.step_count += 1
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# Execute action
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self._apply_action(action)
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# Compute reward
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reward = self._compute_reward()
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# Check if episode is done
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done = self._is_done()
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# Get new observation
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observation = self._get_observation()
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# Additional info
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info = {}
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if done:
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# Include episode statistics when episode ends
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info['episode'] = {
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'r': reward,
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'l': self.step_count
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}
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return observation, reward, done, info
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def _apply_action(self, action):
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"""Apply action to update environment state."""
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# Discrete actions: 0=up, 1=right, 2=down, 3=left
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if action == 0: # Up
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self.agent_pos[1] = min(self.agent_pos[1] + 1, self.grid_size - 1)
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elif action == 1: # Right
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self.agent_pos[0] = min(self.agent_pos[0] + 1, self.grid_size - 1)
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elif action == 2: # Down
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self.agent_pos[1] = max(self.agent_pos[1] - 1, 0)
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elif action == 3: # Left
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self.agent_pos[0] = max(self.agent_pos[0] - 1, 0)
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def _compute_reward(self):
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"""Compute reward for current state."""
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# Distance to goal
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distance = np.linalg.norm(self.agent_pos - self.goal_pos)
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# Reward shaping: negative distance + bonus for reaching goal
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reward = -distance / self.grid_size
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# Goal reached
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if distance < 0.5:
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reward += 10.0
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return reward
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def _is_done(self):
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"""Check if episode is complete."""
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# Episode ends if goal reached or max steps exceeded
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distance = np.linalg.norm(self.agent_pos - self.goal_pos)
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goal_reached = distance < 0.5
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timeout = self.step_count >= self.max_steps
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return goal_reached or timeout
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def _get_observation(self):
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"""Generate observation from current state."""
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# Return flat vector observation
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observation = np.concatenate([
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self.agent_pos,
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self.goal_pos
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]).astype(np.float32)
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return observation
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class MultiAgentEnvironment(PufferEnv):
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"""
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Multi-agent environment template.
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Example: Cooperative navigation task where agents must reach individual goals.
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"""
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def __init__(self, buf=None, num_agents=4, grid_size=10, max_steps=1000):
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super().__init__(buf)
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self.num_agents = num_agents
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self.grid_size = grid_size
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self.max_steps = max_steps
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# Per-agent observation space
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self.single_observation_space = self.make_space({
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'position': (2,),
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'goal': (2,),
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'others': (2 * (num_agents - 1),) # Positions of other agents
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})
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# Per-agent action space
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self.single_action_space = self.make_discrete(5) # 4 directions + stay
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# Initialize state
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self.agent_positions = None
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self.goal_positions = None
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self.step_count = 0
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self.reset()
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def reset(self):
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"""Reset all agents."""
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# Random initial positions
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self.agent_positions = np.random.rand(self.num_agents, 2) * self.grid_size
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# Random goal positions
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self.goal_positions = np.random.rand(self.num_agents, 2) * self.grid_size
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self.step_count = 0
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# Return observations for all agents
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return {
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f'agent_{i}': self._get_obs(i)
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for i in range(self.num_agents)
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}
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def step(self, actions):
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"""
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Step all agents.
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Args:
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actions: Dict of {agent_id: action}
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Returns:
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observations: Dict of {agent_id: observation}
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rewards: Dict of {agent_id: reward}
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dones: Dict of {agent_id: done}
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infos: Dict of {agent_id: info}
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"""
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self.step_count += 1
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observations = {}
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rewards = {}
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dones = {}
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infos = {}
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# Update all agents
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for agent_id, action in actions.items():
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agent_idx = int(agent_id.split('_')[1])
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# Apply action
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self._apply_action(agent_idx, action)
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# Generate outputs
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observations[agent_id] = self._get_obs(agent_idx)
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rewards[agent_id] = self._compute_reward(agent_idx)
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dones[agent_id] = self._is_done(agent_idx)
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infos[agent_id] = {}
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# Global done condition
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dones['__all__'] = all(dones.values()) or self.step_count >= self.max_steps
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return observations, rewards, dones, infos
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def _apply_action(self, agent_idx, action):
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"""Apply action for specific agent."""
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if action == 0: # Up
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self.agent_positions[agent_idx, 1] += 1
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elif action == 1: # Right
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self.agent_positions[agent_idx, 0] += 1
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elif action == 2: # Down
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self.agent_positions[agent_idx, 1] -= 1
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elif action == 3: # Left
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self.agent_positions[agent_idx, 0] -= 1
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# action == 4: Stay
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# Clip to grid bounds
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self.agent_positions[agent_idx] = np.clip(
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self.agent_positions[agent_idx],
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0,
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self.grid_size - 1
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)
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def _compute_reward(self, agent_idx):
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"""Compute reward for specific agent."""
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distance = np.linalg.norm(
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self.agent_positions[agent_idx] - self.goal_positions[agent_idx]
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)
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return -distance / self.grid_size
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def _is_done(self, agent_idx):
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"""Check if specific agent is done."""
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distance = np.linalg.norm(
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self.agent_positions[agent_idx] - self.goal_positions[agent_idx]
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)
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return distance < 0.5
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def _get_obs(self, agent_idx):
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"""Get observation for specific agent."""
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# Get positions of other agents
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other_positions = np.concatenate([
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self.agent_positions[i]
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for i in range(self.num_agents)
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if i != agent_idx
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])
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return {
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'position': self.agent_positions[agent_idx].astype(np.float32),
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'goal': self.goal_positions[agent_idx].astype(np.float32),
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'others': other_positions.astype(np.float32)
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}
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def test_environment():
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"""Test environment to verify it works correctly."""
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print("Testing single-agent environment...")
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env = MyEnvironment()
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obs = env.reset()
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print(f"Initial observation shape: {obs.shape}")
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for step in range(10):
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action = env.action_space.sample()
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obs, reward, done, info = env.step(action)
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print(f"Step {step}: reward={reward:.3f}, done={done}")
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if done:
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obs = env.reset()
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print("Episode finished, resetting...")
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print("\nTesting multi-agent environment...")
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multi_env = MultiAgentEnvironment(num_agents=4)
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obs = multi_env.reset()
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print(f"Number of agents: {len(obs)}")
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for step in range(10):
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actions = {
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agent_id: multi_env.single_action_space.sample()
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for agent_id in obs.keys()
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}
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obs, rewards, dones, infos = multi_env.step(actions)
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print(f"Step {step}: mean_reward={np.mean(list(rewards.values())):.3f}")
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if dones.get('__all__', False):
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obs = multi_env.reset()
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print("Episode finished, resetting...")
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print("\n✓ Environment tests passed!")
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if __name__ == '__main__':
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test_environment()
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