from math import inf
import torch
from torch import jit
# Model-predictive control planner with cross-entropy method and learned transition model
class MPCPlanner(jit.ScriptModule):
__constants__ = ['action_size', 'planning_horizon', 'optimisation_iters', 'candidates', 'top_candidates', 'min_action', 'max_action']
def __init__(self, action_size, planning_horizon, optimisation_iters, candidates, top_candidates, transition_model, reward_model, min_action=-inf, max_action=inf):
super().__init__()
self.transition_model, self.reward_model = transition_model, reward_model
self.action_size, self.min_action, self.max_action = action_size, min_action, max_action
self.planning_horizon = planning_horizon
self.optimisation_iters = optimisation_iters
self.candidates, self.top_candidates = candidates, top_candidates
@jit.script_method
def forward(self, belief, state):
B, H, Z = belief.size(0), belief.size(1), state.size(1)
belief, state = belief.unsqueeze(dim=1).expand(B, self.candidates, H).reshape(-1, H), state.unsqueeze(dim=1).expand(B, self.candidates, Z).reshape(-1, Z)
# Initialize factorized belief over action sequences q(a_t:t+H) ~ N(0, I)
action_mean, action_std_dev = torch.zeros(self.planning_horizon, B, 1, self.action_size, device=belief.device), torch.ones(self.planning_horizon, B, 1, self.action_size, device=belief.device)
for _ in range(self.optimisation_iters):
# Evaluate J action sequences from the current belief (over entire sequence at once, batched over particles)
actions = (action_mean + action_std_dev * torch.randn(self.planning_horizon, B, self.candidates, self.action_size, device=action_mean.device)).view(self.planning_horizon, B * self.candidates, self.action_size) # Sample actions (time x (batch x candidates) x actions)
actions.clamp_(min=self.min_action, max=self.max_action) # Clip action range
# Sample next states
beliefs, states, _, _ = self.transition_model(state, actions, belief)
# Calculate expected returns (technically sum of rewards over planning horizon)
returns = self.reward_model(beliefs.view(-1, H), states.view(-1, Z)).view(self.planning_horizon, -1).sum(dim=0)
# Re-fit belief to the K best action sequences
_, topk = returns.reshape(B, self.candidates).topk(self.top_candidates, dim=1, largest=True, sorted=False)
topk += self.candidates * torch.arange(0, B, dtype=torch.int64, device=topk.device).unsqueeze(dim=1) # Fix indices for unrolled actions
best_actions = actions[:, topk.view(-1)].reshape(self.planning_horizon, B, self.top_candidates, self.action_size)
# Update belief with new means and standard deviations
action_mean, action_std_dev = best_actions.mean(dim=2, keepdim=True), best_actions.std(dim=2, unbiased=False, keepdim=True)
# Return first action mean ยต_t
return action_mean[0].squeeze(dim=1)
