From: François Fleuret Date: Mon, 25 Mar 2024 20:59:57 +0000 (+0100) Subject: Update. X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=picoclvr.git;a=commitdiff_plain;h=0b8185b90014369f0d39892e128ad04a7d9ae872 Update. --- diff --git a/escape.py b/escape.py index 8066479..a3d8c85 100755 --- a/escape.py +++ b/escape.py @@ -11,7 +11,7 @@ from torch.nn import functional as F ###################################################################### -nb_states_codes = 4 +nb_states_codes = 5 nb_actions_codes = 5 nb_rewards_codes = 3 nb_lookahead_rewards_codes = 3 @@ -60,14 +60,13 @@ def code2lookahead_reward(r): ###################################################################### -def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3): +def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3, nb_coins=3): rnd = torch.rand(nb, height, width) rnd[:, 0, :] = 0 rnd[:, -1, :] = 0 rnd[:, :, 0] = 0 rnd[:, :, -1] = 0 wall = 0 - for k in range(nb_walls): wall = wall + ( rnd.flatten(1).argmax(dim=1)[:, None] @@ -76,6 +75,17 @@ def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3): rnd = rnd * (1 - wall.clamp(max=1)) + rnd = torch.rand(nb, height, width) + coins = torch.zeros(nb, T, height, width, dtype=torch.int64) + rnd = rnd * (1 - wall.clamp(max=1)) + for k in range(nb_coins): + coins[:, 0] = coins[:, 0] + ( + rnd.flatten(1).argmax(dim=1)[:, None] + == torch.arange(rnd.flatten(1).size(1))[None, :] + ).long().reshape(rnd.size()) + + rnd = rnd * (1 - coins[:, 0].clamp(max=1)) + states = wall[:, None, :, :].expand(-1, T, -1, -1).clone() agent = torch.zeros(states.size(), dtype=torch.int64) @@ -131,9 +141,12 @@ def generate_episodes(nb, height=6, width=6, T=10, nb_walls=3): # assert hit.min() == 0 and hit.max() <= 1 - rewards[:, t + 1] = -hit + (1 - hit) * agent[:, t + 1, -1, -1] + got_coin = (agent[:, t + 1] * coins[:, t]).flatten(1).sum(dim=1) + coins[:, t + 1] = coins[:, t] * (1 - agent[:, t + 1]) + + rewards[:, t + 1] = -hit + (1 - hit) * got_coin - states += 2 * agent + 3 * monster + states = states + 2 * agent + 3 * monster + 4 * coins return states, agent_actions, rewards @@ -173,7 +186,7 @@ def seq2episodes(seq, height, width): def seq2str(seq): def token2str(t): if t >= first_states_code and t < first_states_code + nb_states_codes: - return " #@$"[t - first_states_code] + return " #@T$"[t - first_states_code] elif t >= first_actions_code and t < first_actions_code + nb_actions_codes: return "ISNEW"[t - first_actions_code] elif t >= first_rewards_code and t < first_rewards_code + nb_rewards_codes: @@ -196,11 +209,11 @@ def episodes2str( lookahead_rewards, states, actions, rewards, unicode=False, ansi_colors=False ): if unicode: - symbols = "·█@$" + symbols = "·█@T$" # vert, hori, cross, thin_hori = "║", "═", "╬", "─" vert, hori, cross, thin_vert, thin_hori = "┃", "━", "╋", "│", "─" else: - symbols = " #@$" + symbols = " #@T$" vert, hori, cross, thin_vert, thin_hori = "|", "-", "+", "|", "-" hline = (cross + hori * states.size(-1)) * states.size(1) + cross + "\n" @@ -266,11 +279,11 @@ def episodes2str( ###################################################################### if __name__ == "__main__": - nb, height, width, T, nb_walls = 5, 5, 7, 4, 5 + nb, height, width, T, nb_walls = 5, 5, 7, 10, 5 states, actions, rewards = generate_episodes(nb, height, width, T, nb_walls) seq = episodes2seq(states, actions, rewards) lr, s, a, r = seq2episodes(seq, height, width) print(episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)) - print() - for s in seq2str(seq): - print(s) + # print() + # for s in seq2str(seq): + # print(s) diff --git a/tasks.py b/tasks.py index f2b7709..11879fd 100755 --- a/tasks.py +++ b/tasks.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import math, os, tqdm +import math, os, tqdm, warnings import torch, torchvision @@ -1928,6 +1928,8 @@ class Escape(Task): result[:, it_len:] = -1 + snapshots = [] + def ar(result, ar_mask, logit_biases=None): ar_mask = ar_mask.expand_as(result) result *= 1 - ar_mask @@ -1941,6 +1943,8 @@ class Escape(Task): device=self.device, progress_bar_desc=None, ) + warnings.warn("keeping thinking snapshots", RuntimeWarning) + snapshots.append(result[:10].detach().clone()) # Generate iteration after iteration @@ -1948,30 +1952,32 @@ class Escape(Task): optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1) optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1) - snapshots = [] - for u in tqdm.tqdm( range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking" ): + lr, _, _, _ = escape.seq2episodes(result[:, :u], self.height, self.width) + # Generate the lookahead_reward and state - ar_mask = (t >= u + index_lookahead_reward).long() * ( + ar_mask = (t % it_len == index_lookahead_reward).long() * ( + t <= u + index_lookahead_reward + ).long() + ar(result, ar_mask) + + # Generate the lookahead_reward and state + ar_mask = (t >= u + index_states).long() * ( t < u + index_states + state_len ).long() ar(result, ar_mask) - snapshots.append(result[:10].detach().clone()) - backup_lookahead_reward = result[:, u + index_lookahead_reward] # Re-generate the lookahead_reward - ar_mask = (t == u + index_lookahead_reward).long() + ar_mask = (t % it_len == index_lookahead_reward).long() * ( + t <= u + index_lookahead_reward + ).long() ar(result, ar_mask, logit_biases=optimistic_bias) - snapshots.append(result[:10].detach().clone()) # Generate the action and reward ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long() ar(result, ar_mask) - snapshots.append(result[:10].detach().clone()) - - result[:, u + index_lookahead_reward] = backup_lookahead_reward filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt") with open(filename, "w") as f: