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Merge pull request google-deepmind#1038 from giogix2:eligibility_trac…
…es_for_tabular_q_learning PiperOrigin-RevId: 523971457 Change-Id: Ica15bfa720a64b10b4386b6aa754cf8f64d0c6b9
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| // Copyright 2023 DeepMind Technologies Limited | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #include "open_spiel/algorithms/tabular_q_learning.h" | ||
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| #include <memory> | ||
| #include <string> | ||
| #include <utility> | ||
| #include <vector> | ||
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| #include "open_spiel/abseil-cpp/absl/random/distributions.h" | ||
| #include "open_spiel/abseil-cpp/absl/random/random.h" | ||
| #include "open_spiel/games/catch.h" | ||
| #include "open_spiel/spiel.h" | ||
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| namespace open_spiel { | ||
| namespace { | ||
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| Action GetOptimalAction( | ||
| absl::flat_hash_map<std::pair<std::string, Action>, double> q_values, | ||
| const std::unique_ptr<State> &state) { | ||
| std::vector<Action> legal_actions = state->LegalActions(); | ||
| const auto state_str = state->ToString(); | ||
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| Action optimal_action = open_spiel::kInvalidAction; | ||
| double value = -1; | ||
| for (const Action &action : legal_actions) { | ||
| double q_val = q_values[{state_str, action}]; | ||
| if (q_val >= value) { | ||
| value = q_val; | ||
| optimal_action = action; | ||
| } | ||
| } | ||
| return optimal_action; | ||
| } | ||
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| Action GetRandomAction(const std::unique_ptr<State> &state, int seed) { | ||
| std::vector<Action> legal_actions = state->LegalActions(); | ||
| if (legal_actions.empty()) { | ||
| return kInvalidAction; | ||
| } | ||
| std::mt19937 rng(seed); | ||
| return legal_actions[absl::Uniform<int>(rng, 0, legal_actions.size())]; | ||
| } | ||
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| double PlayCatch( | ||
| absl::flat_hash_map<std::pair<std::string, Action>, double> q_values, | ||
| const std::unique_ptr<State> &state, double seed) { | ||
| // First action determines the starting column. Do the first action before the | ||
| // main loop, where the optimal action is chosen. | ||
| // Example: Initial state with random seed 42 | ||
| // ...o. | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..... | ||
| // ..x.. | ||
| std::mt19937 gen(seed); | ||
| std::uniform_int_distribution<int> distribution(0, | ||
| catch_::kDefaultColumns - 1); | ||
| int ball_starting_column = distribution(gen); | ||
| state->ApplyAction(ball_starting_column); | ||
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| while (!state->IsTerminal()) { | ||
| Action optimal_action = GetOptimalAction(q_values, state); | ||
| state->ApplyAction(optimal_action); | ||
| } | ||
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| return state->Rewards()[0]; | ||
| } | ||
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| std::unique_ptr<open_spiel::algorithms::TabularQLearningSolver> QLearningSolver( | ||
| std::shared_ptr<const Game> game, double lambda) { | ||
| return std::make_unique<open_spiel::algorithms::TabularQLearningSolver>( | ||
| /*game=*/game, | ||
| /*depth_limit=*/-1.0, | ||
| /*epsilon=*/0.1, | ||
| /*learning_rate=*/0.01, | ||
| /*discount_factor=*/0.99, | ||
| /*lambda=*/lambda); | ||
| } | ||
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| void TabularQLearningTest_Catch_Lambda00_Loss() { | ||
| // Classic Q-learning. No bootstraping (lambda=0.0) | ||
| // Player loses after only 1 train iteration. | ||
| std::shared_ptr<const Game> game = LoadGame("catch"); | ||
| auto tabular_q_learning_solver = QLearningSolver(game, 0); | ||
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| tabular_q_learning_solver->RunIteration(); | ||
| const absl::flat_hash_map<std::pair<std::string, Action>, double>& q_values = | ||
| tabular_q_learning_solver->GetQValueTable(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
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| double reward = PlayCatch(q_values, state, 42); | ||
| SPIEL_CHECK_EQ(reward, -1); | ||
| } | ||
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| void TabularQLearningTest_Catch_Lambda00_Win() { | ||
| // Classic Q-learning. No bootstraping (lambda=0.0) | ||
| // Player wins after 100 train iterations | ||
| std::shared_ptr<const Game> game = LoadGame("catch"); | ||
| auto tabular_q_learning_solver = QLearningSolver(game, 0); | ||
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| for (int i = 1; i < 100; i++) { | ||
| tabular_q_learning_solver->RunIteration(); | ||
| } | ||
| const absl::flat_hash_map<std::pair<std::string, Action>, double>& q_values = | ||
| tabular_q_learning_solver->GetQValueTable(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
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| double reward = PlayCatch(q_values, state, 42); | ||
| SPIEL_CHECK_EQ(reward, 1); | ||
| } | ||
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| void TabularQLearningTest_Catch_Lambda01_Win() { | ||
| // Player wins after 100 train iterations | ||
| std::shared_ptr<const Game> game = LoadGame("catch"); | ||
| auto tabular_q_learning_solver = QLearningSolver(game, 0.1); | ||
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| for (int i = 1; i < 100; i++) { | ||
| tabular_q_learning_solver->RunIteration(); | ||
| } | ||
| const absl::flat_hash_map<std::pair<std::string, Action>, double>& q_values = | ||
| tabular_q_learning_solver->GetQValueTable(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
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| double reward = PlayCatch(q_values, state, 42); | ||
| SPIEL_CHECK_EQ(reward, 1); | ||
| } | ||
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| void TabularQLearningTest_Catch_Lambda01FasterThanLambda00() { | ||
| // Eligibility traces (lambda > 0.0) always achieves victory with less | ||
| // training steps w.r.t. Q-learning(lambda=0.0) | ||
| std::shared_ptr<const Game> game = LoadGame("catch"); | ||
| auto tabular_q_learning_solver_lambda00 = QLearningSolver(game, 0); | ||
| auto tabular_q_learning_solver_lambda01 = QLearningSolver(game, 0.1); | ||
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| for (int seed = 0; seed < 100; seed++) { | ||
| int lambda_00_train_iter = 0; | ||
| int lambda_01_train_iter = 0; | ||
| double lambda_00_reward = -1.0; | ||
| double lambda_01_reward = -1.0; | ||
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| while (lambda_00_reward == -1.0) { | ||
| tabular_q_learning_solver_lambda00->RunIteration(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
| lambda_00_reward = PlayCatch( | ||
| tabular_q_learning_solver_lambda00->GetQValueTable(), state, seed); | ||
| lambda_00_train_iter++; | ||
| } | ||
| while (lambda_01_reward == -1.0) { | ||
| tabular_q_learning_solver_lambda01->RunIteration(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
| lambda_01_reward = PlayCatch( | ||
| tabular_q_learning_solver_lambda01->GetQValueTable(), state, seed); | ||
| lambda_01_train_iter++; | ||
| } | ||
| SPIEL_CHECK_GE(lambda_00_train_iter, lambda_01_train_iter); | ||
| } | ||
| } | ||
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| void TabularQLearningTest_TicTacToe_Lambda01_Win() { | ||
| std::shared_ptr<const Game> game = open_spiel::LoadGame("tic_tac_toe"); | ||
| auto tabular_q_learning_solver = QLearningSolver(game, 0.1); | ||
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| for (int i = 1; i < 100; i++) { | ||
| tabular_q_learning_solver->RunIteration(); | ||
| } | ||
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| const absl::flat_hash_map<std::pair<std::string, Action>, double>& q_values = | ||
| tabular_q_learning_solver->GetQValueTable(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
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| while (!state->IsTerminal()) { | ||
| Action random_action = GetRandomAction(state, 42); | ||
| state->ApplyAction(random_action); // player 0 | ||
| if (random_action == kInvalidAction) break; | ||
| state->ApplyAction(GetOptimalAction(q_values, state)); // player 1 | ||
| } | ||
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| SPIEL_CHECK_EQ(state->Rewards()[0], -1); | ||
| } | ||
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| void TabularQLearningTest_TicTacToe_Lambda01_Tie() { | ||
| std::shared_ptr<const Game> game = open_spiel::LoadGame("tic_tac_toe"); | ||
| auto tabular_q_learning_solver = QLearningSolver(game, 0.1); | ||
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| for (int i = 1; i < 1000; i++) { | ||
| tabular_q_learning_solver->RunIteration(); | ||
| } | ||
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| const absl::flat_hash_map<std::pair<std::string, Action>, double>& q_values = | ||
| tabular_q_learning_solver->GetQValueTable(); | ||
| std::unique_ptr<State> state = game->NewInitialState(); | ||
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| while (!state->IsTerminal()) { | ||
| state->ApplyAction(GetOptimalAction(q_values, state)); | ||
| } | ||
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| SPIEL_CHECK_EQ(state->Rewards()[0], 0); | ||
| } | ||
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| } // namespace | ||
| } // namespace open_spiel | ||
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| int main(int argc, char **argv) { | ||
| open_spiel::TabularQLearningTest_Catch_Lambda00_Loss(); | ||
| open_spiel::TabularQLearningTest_Catch_Lambda00_Win(); | ||
| open_spiel::TabularQLearningTest_Catch_Lambda01_Win(); | ||
| open_spiel::TabularQLearningTest_Catch_Lambda01FasterThanLambda00(); | ||
| open_spiel::TabularQLearningTest_TicTacToe_Lambda01_Win(); | ||
| open_spiel::TabularQLearningTest_TicTacToe_Lambda01_Tie(); | ||
| } |
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