From 948ae9af4fba88343c4065df462219ab67cf17c7 Mon Sep 17 00:00:00 2001 From: Vasilis Valatsos Date: Fri, 8 Dec 2023 22:08:25 +0100 Subject: [PATCH] Once again player with rewards, and added clipping to the params --- agents/ppo/agent.py | 26 ++++++++++- agents/ppo/utils/hyperparams.py | 6 --- agents/saved_models/A0 | Bin 458006 -> 0 bytes agents/saved_models/A1 | Bin 304406 -> 0 bytes agents/saved_models/C0 | Bin 452822 -> 0 bytes agents/saved_models/C1 | Bin 299222 -> 0 bytes assets/map/Entities.csv | 58 +++++++++++------------ configs/game/monster_config.py | 26 +++++------ configs/game/player_config.py | 12 ++--- entities/player.py | 64 +++++++++++++------------- figures/actor_loss.png | Bin 51858 -> 13344 bytes figures/critic_loss.png | Bin 32965 -> 13156 bytes figures/older_figures/actor_loss.png | Bin 0 -> 24469 bytes figures/older_figures/critic_loss.png | Bin 0 -> 25511 bytes figures/older_figures/score.png | Bin 0 -> 56350 bytes figures/older_figures/total_loss.png | Bin 0 -> 24451 bytes figures/score.png | Bin 22888 -> 15811 bytes figures/total_loss.png | Bin 29808 -> 20279 bytes level.py | 50 +++++++++++--------- pneuma.py | 13 ++++-- 20 files changed, 142 insertions(+), 113 deletions(-) delete mode 100644 agents/ppo/utils/hyperparams.py delete mode 100644 agents/saved_models/A0 delete mode 100644 agents/saved_models/A1 delete mode 100644 agents/saved_models/C0 delete mode 100644 agents/saved_models/C1 create mode 100644 figures/older_figures/actor_loss.png create mode 100644 figures/older_figures/critic_loss.png create mode 100644 figures/older_figures/score.png create mode 100644 figures/older_figures/total_loss.png diff --git a/agents/ppo/agent.py b/agents/ppo/agent.py index f287f78..ebc7bba 100644 --- a/agents/ppo/agent.py +++ b/agents/ppo/agent.py @@ -8,12 +8,13 @@ class Agent: def __init__(self, input_dims, n_actions, gamma=0.99, alpha=0.0003, policy_clip=0.2, batch_size=64, N=2048, n_epochs=10, - gae_lambda=0.95, chkpt_dir='tmp/ppo'): + gae_lambda=0.95, entropy_coef=0.001, chkpt_dir='tmp/ppo'): self.gamma = gamma self.policy_clip = policy_clip self.n_epochs = n_epochs self.gae_lambda = gae_lambda + self.entropy_coef = entropy_coef self.actor = ActorNetwork( input_dims, n_actions, alpha, chkpt_dir=chkpt_dir) @@ -44,6 +45,8 @@ class Agent: action = T.squeeze(action).item() value = T.squeeze(value).item() + self.entropy = dist.entropy().mean().item() + return action, probs, value def learn(self): @@ -81,8 +84,10 @@ class Agent: new_probs = dist.log_prob(actions) prob_ratio = new_probs.exp() / old_probs.exp() weighted_probs = advantage[batch] * prob_ratio + weighted_clipped_probs = T.clamp( prob_ratio, 1-self.policy_clip, 1+self.policy_clip)*advantage[batch] + self.actor_loss = -T.min(weighted_probs, weighted_clipped_probs).mean() @@ -90,10 +95,27 @@ class Agent: self.critic_loss = (returns - critic_value)**2 self.critic_loss = self.critic_loss.mean() - self.total_loss = self.actor_loss + 0.5*self.critic_loss + self.total_loss = self.actor_loss + 0.5 * \ + self.critic_loss - self.entropy_coef*self.entropy + self.actor.optimizer.zero_grad() self.critic.optimizer.zero_grad() self.total_loss.backward() + + # Calculate the gradient norms for both networks + actor_grad_norm = T.nn.utils.clip_grad_norm_( + self.actor.parameters(), max_norm=1) + critic_grad_norm = T.nn.utils.clip_grad_norm_( + self.critic.parameters(), max_norm=1) + + T.nn.utils.clip_grad_norm_( + self.actor.parameters(), max_norm=1) + T.nn.utils.clip_grad_norm_( + self.critic.parameters(), max_norm=1) + # Log or print the gradient norms + print(f"Actor Gradient Norm: {actor_grad_norm}") + print(f"Critic Gradient Norm: {critic_grad_norm}") + self.actor.optimizer.step() self.critic.optimizer.step() diff --git a/agents/ppo/utils/hyperparams.py b/agents/ppo/utils/hyperparams.py deleted file mode 100644 index aca1cfd..0000000 --- a/agents/ppo/utils/hyperparams.py +++ /dev/null @@ -1,6 +0,0 @@ -# AI setup -N = 20 -batch_size = 5 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