Trying stuff
@@ -17,7 +17,7 @@ LEARNING_RATE = 0.001
ENTROPY_BETA = 0.01
BATCH_SIZE = 128
-REWARD_STEPS = 100
+REWARD_STEPS = 10
BASELINE_STEPS = 10000
@@ -41,7 +41,7 @@ if __name__ == "__main__":
agent = ptan.agent.PolicyAgent(net, apply_softmax=True, cuda=args.cuda)
exp_source = ptan.experience.ExperienceSourceFirstLast(envs, agent, gamma=GAMMA, steps_count=REWARD_STEPS)
- optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
+ optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE, eps=1e-3)
total_rewards = []
step_rewards = []
