Env_name stringclasses 9
values | content stringclasses 9
values |
|---|---|
simple.txt |
{
"AgentFunctions": {
"agent_reward": "def reward(self, agent, world): dist2 = np.sum(np.square(agent.state.p_pos - world.landmarks[0].state.p_pos)); return -dist2",
"observation": "def observation(self, agent, world): entity_pos = [entity.state.p_pos - agent.state.p_pos for entity in world.land... |
simple_adversary.txt | {
"AgentFunctions": {
"agent_reward": "def agent_reward(self, agent, world): shaped_reward, shaped_adv_reward = True, True; adversary_agents, good_agents = self.adversaries(world), self.good_agents(world); adv_rew = sum(np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) for a in adversary_agen... |
simple_crypto.txt | {
"AgentFunctions": {
"agent_reward": "def agent_reward(self, agent, world): good_listeners, adversaries = self.good_listeners(world), self.adversaries(world); good_rew, adv_rew = 0, 0; good_rew = sum(0 if (a.state.c == np.zeros(world.dim_c)).all() else -np.sum(np.square(a.state.c - agent.goal_a.color)) for... |
simple_push.txt | {
"AgentFunctions": {
"agent_reward": "def agent_reward(self, agent, world): return -np.sqrt(np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos))) # the distance to the goal",
"adversary_reward": "def adversary_reward(self, agent, world): agent_dist = [np.sqrt(np.sum(np.square(a.state.p_... |
simple_reference.txt | {
"AgentFunctions": {
"agent_reward": "def reward(self, agent, world): agent_reward = 0.0 if agent.goal_a is None or agent.goal_b is None else np.sqrt(np.sum(np.square(agent.goal_a.state.p_pos - agent.goal_b.state.p_pos))); return -agent_reward \n def global_reward(self, world): all_rewards = sum(self.rewar... |
simple_speaker_listener.txt | {
"AgentFunctions": {
"agent_reward": "",
"adversary_reward": "def reward(self, agent, world): a = world.agents[0]; dist2 = np.sum(np.square(a.goal_a.state.p_pos - a.goal_b.state.p_pos)); return -dist2 # squared distance from listener to landmark",
"observation": "def observation(self, agen... |
simple_spread.txt | {
"AgentFunctions": {
"agent_reward": "def is_collision(self, agent1, agent2): delta_pos = agent1.state.p_pos - agent2.state.p_pos; dist = np.sqrt(np.sum(np.square(delta_pos))); dist_min = agent1.size + agent2.size; return True if dist < dist_min else False \n def reward(self, agent, world): rew = 0; rew -=... |
simple_tag.txt | {
"AgentFunctions": {
"agent_reward": "def agent_reward(self, agent, world): rew = 0; shape = False; adversaries = self.adversaries(world); rew += sum(0.1 * np.sqrt(np.sum(np.square(agent.state.p_pos - adv.state.p_pos))) for adv in adversaries) if shape else 0; rew -= sum(10 for a in adversaries if agent.co... |
simple_world_comm.txt | {
"AgentFunctions": {
"agent_reward": "def agent_reward(self, agent, world): rew = 0; shape = False; adversaries = self.adversaries(world); rew += sum(0.1 * np.sqrt(np.sum(np.square(agent.state.p_pos - adv.state.p_pos))) for adv in adversaries) if shape else 0; rew -= sum(5 for a in adversaries if agent.col... |
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